Condition monitoring is sometimes portrayed in industry messaging as being effectively limited to vibration analysis and ultrasound. Such claims, including those recently advanced by emerging vendors in the sensing technology space, reflect a broader pattern of marketing-driven simplification rather than technical accuracy. International standards define condition monitoring as a process that incorporates multiple diagnostic techniques, including lubricant analysis, vibration, thermography, and electrical methods ¹.

Reliability-centered maintenance theory further emphasizes that different technologies are required to detect distinct failure modes and stages of degradation ². Empirical research in predictive maintenance consistently demonstrates that no single method provides complete fault coverage across all mechanical systems ³. These foundational perspectives establish that condition monitoring must be understood as an integrated, multi-technology framework rather than a simplified binary construct.

Condition monitoring must be understood as an integrated, multi-technology framework rather than a simplified binary construct.

Oil analysis is a critical component of this framework because it can detect early-stage degradation and identify the root causes of failure. Oil analysis is formally recognized as a primary condition monitoring method capable of evaluating lubricant condition, contamination, and wear debris as indicators of machine health ¹. Standardized testing methods developed by ASTM provide consistent procedures for assessing these parameters across a wide range of equipment types ⁷.

In particular, tribological research demonstrates that wear particle generation occurs at the onset of failure mechanisms, often preceding measurable changes in vibration signatures ⁴. Furthermore, applied studies in machinery diagnostics show that oil analysis frequently identifies incipient faults earlier than vibration-based methods in rotating equipment systems ⁶. Accordingly, this article directly challenges reductionist claims by demonstrating that condition monitoring is inherently multi-modal and that oil analysis plays a critical, often leading role in fault detection and diagnosis.

Condition Monitoring as a Multi-Technology Framework

Standards and Established Practice

Condition monitoring is fundamentally structured as a multi-technology discipline designed to capture different dimensions of machine degradation. ISO 17359 explicitly identifies lubricant analysis as a primary condition-monitoring technique, alongside vibration and other diagnostic methods ¹. Furthermore, reliability-centered maintenance frameworks reinforce that the selection of monitoring technologies must align with specific failure mechanisms rather than convenience or convention ².

In addition, industry research demonstrates that combining multiple technologies significantly improves fault detection accuracy and reduces the risk of missed failures ³. Taken together, these perspectives confirm that any attempt to reduce condition monitoring to a limited subset of technologies is inconsistent with established practice.

Any attempt to reduce condition monitoring to a limited subset of technologies is inconsistent with established practice.

This broader context underscores the need to examine how different technologies contribute uniquely to fault detection.

Failure Mechanism Complexity

The necessity of multiple technologies becomes more evident when considering the complexity of failure mechanisms in rotating equipment. Mechanical systems experience degradation through processes such as wear, fatigue, corrosion, and contamination ⁸. Notably, each process produces distinct physical and chemical signatures that are not uniformly detectable by a single monitoring method ⁵.

Moreover, empirical studies have shown that certain faults remain undetected when relying exclusively on vibration or acoustic methods ⁹. Consequently, this complexity reinforces—not merely suggests—the need for oil analysis within a comprehensive monitoring strategy.

Oil Analysis as a Foundational Diagnostic Method

Lubricant Condition and Wear Assessment

Oil analysis serves as a foundational diagnostic method by providing direct insight into both lubricant condition and machine wear. Standard practices defined by ASTM establish oil analysis as a structured approach for monitoring viscosity, oxidation, contamination, and wear metals ⁷. In particular, tribological research confirms that lubricants act as carriers of wear debris and contaminants, effectively transporting evidence of internal machine conditions ⁴.

Additionally, engineering studies demonstrate that oil analysis enables simultaneous evaluation of mechanical and chemical degradation processes ⁵. As a result, this dual capability distinguishes oil analysis from external sensing methods that rely solely on energy measurement. This distinction becomes particularly important when precise diagnostic resolution is required for effective maintenance decisions.

Characterizing Wear Mechanisms

The diagnostic depth of oil analysis is further enhanced by its ability to characterize wear mechanisms. Analytical ferrography and particle analysis techniques allow for the identification of wear modes such as abrasion, adhesion, and fatigue ⁹. Likewise, elemental spectroscopy provides additional resolution by linking detected metals to specific machine components ⁶.

Together, these capabilities enable practitioners to move beyond fault detection and toward precise failure diagnosis. Consequently, this level of diagnostic resolution establishes oil analysis as a critical tool for understanding the underlying causes of machine degradation.

Early Fault Detection and the Failure Progression Curve

Detection at the Point of Origin

Oil analysis enables earlier fault detection by identifying degradation at the point of origin within the machine. Wear particles are generated during the initial stages of material interaction, often before significant energy is produced ⁴.

In fact, empirical studies have shown that wear debris analysis can detect bearing and gear faults months in advance of vibration-based detection thresholds ⁶. In applied industrial settings, advanced lubricant data analysis has demonstrated the potential to extend this detection window significantly, in some cases approaching multiple years of advanced indication.

Wear debris analysis can detect bearing and gear faults months in advance of vibration-based detection thresholds.

Furthermore, additional research indicates that early-stage contamination and lubricant degradation can be identified before they result in measurable mechanical symptoms ⁵. As a result, these findings demonstrate that oil analysis operates at the earliest portion of the failure progression curve. This early positioning is best understood within the context of reliability engineering models.

The P–F Interval Advantage

The temporal advantage of oil analysis is best understood within the context of the P–F interval. Reliability literature defines the P–F interval as the time between detectable potential failure and functional failure ². Importantly, technologies that detect faults earlier within this interval provide a greater opportunity for corrective action and risk mitigation ³.

In contrast, comparative studies have shown that vibration analysis often detects faults at later stages when damage has progressed sufficiently to affect machine dynamics ⁹. Therefore, this positioning highlights the strategic value of oil analysis in extending the predictive maintenance window.

Root Cause Identification and Diagnostic Resolution

Particle Morphology and Elemental Analysis

Oil analysis provides diagnostic resolution that enables the identification of root causes of failure. Wear particle morphology allows analysts to distinguish between different wear mechanisms based on particle size, shape, and texture ⁴. Elemental analysis further supports root cause identification by associating specific metals with machine components ⁶.

Contamination analysis reveals external influences such as dirt ingress or water contamination that contribute to accelerated wear ⁵. These capabilities allow oil analysis to move beyond symptom detection and toward causal diagnosis. This ability to identify underlying causes has direct implications for maintenance effectiveness.

From Symptoms to Causes

The ability to identify root causes has significant implications for maintenance strategy. Corrective actions based on root cause analysis are more effective than those based solely on symptom detection ³. Studies in reliability engineering have demonstrated that addressing underlying causes reduces recurrence rates and improves equipment lifespan ².

Corrective actions based on root cause analysis are more effective than those based solely on symptom detection.

In contrast, technologies that primarily detect symptoms may require additional analysis to determine the source of failure ⁹. This distinction reinforces the importance of oil analysis within a comprehensive diagnostic framework.

Internal Access Versus External Measurement

The Lubricant as a Diagnostic Medium

Oil analysis provides direct access to the internal operating environment of machinery. Lubricants circulate through critical components, collecting information about wear, contamination, and chemical changes ⁵. As such, tribological studies confirm that this internal perspective allows for the detection of conditions that are not immediately observable through external measurement ⁸.

Moreover, wear debris transported in the lubricant reflects real-time interactions occurring at the surface level of machine components ⁴. Consequently, this internal visibility provides a unique diagnostic advantage. This advantage becomes more apparent when contrasted with external sensing approaches.

Limitations of External Sensing

External sensing technologies, including vibration and ultrasound, rely on detecting energy transmitted through machine structures. By comparison, these methods require faults to reach a severity level that produces measurable signals ⁹. Additionally, signal interpretation can be influenced by factors such as machine geometry and operating conditions ³.

As a result, certain early-stage faults may remain undetected until they progress further. This contrast highlights the complementary nature of internal and external monitoring approaches, particularly when evaluating the limitations of any single diagnostic method.

Limitations of Vibration and Ultrasound as Exclusive Solutions

Detection Gaps in Single-Technology Approaches

Vibration and ultrasound are valuable diagnostic tools, but are limited when used as standalone solutions. Vibration analysis is highly effective for detecting imbalance, misalignment, and looseness, but is less sensitive to early-stage wear in low-energy conditions ³. Ultrasound can detect friction-related phenomena but provides limited information regarding wear mechanisms and contamination sources ⁹.

Research has shown that reliance on a single technology increases the likelihood of missed or delayed fault detection ². These limitations underscore the need for a multi-technology approach. This recognition leads directly to the importance of integrating complementary diagnostic methods.

Closing the Gap with Oil Analysis

The integration of oil analysis addresses many of these limitations by providing complementary data. Oil analysis captures early-stage degradation and identifies root causes, while vibration and ultrasound provide information about fault severity and dynamic behavior ⁵.

Studies in predictive maintenance demonstrate that combining these methods improves diagnostic accuracy and maintenance decision-making ³. This integrated approach aligns with best practices in reliability engineering and forms the basis for modern condition monitoring strategies.

Integration of Technologies in Modern Reliability Practice

Complementary Diagnostics as Core Principle

Modern reliability practice formalizes the integration of multiple condition monitoring technologies as a core operational principle. Reliability-centered maintenance frameworks advocate for the use of complementary diagnostic tools to address different failure modes ². Industry research demonstrates that integrated monitoring programs achieve higher reliability and lower maintenance costs than single-technology approaches ³.

Tribological and mechanical studies confirm that combining internal and external monitoring methods provides a more complete understanding of machine condition ⁸. These findings support a holistic approach to condition monitoring. This holistic approach is essential for maximizing diagnostic effectiveness.

Building a Comprehensive Diagnostic System

The integration of oil analysis with vibration and ultrasound creates a comprehensive diagnostic system. Oil analysis provides early detection and root cause identification, while vibration and ultrasound assess fault progression and severity ⁵. This combination enables more informed maintenance decisions and reduces the risk of unexpected failures ⁹.

Integrated monitoring programs achieve higher reliability and lower maintenance costs than single-technology approaches.

The resulting synergy enhances both detection capability and diagnostic accuracy. This integrated perspective provides the foundation for evaluating reductionist claims.

Conclusion

The assertion that condition monitoring has been reduced to vibration analysis and ultrasound is inconsistent with established standards, empirical research, and practical application. Oil analysis is recognized as a primary condition-monitoring technology in international standards and provides unique capabilities for early fault detection and root-cause identification ¹. Furthermore, tribological and engineering research demonstrates that oil analysis detects degradation at its origin and offers diagnostic insights not available through external sensing methods ^4,5.

In addition, reliability frameworks confirm that effective condition monitoring requires integrating multiple technologies rather than relying on a single approach ². Therefore, a more accurate and defensible position is that condition monitoring is a multi-technology discipline in which oil analysis plays a critical and often leading role in identifying and diagnosing machine failure.

To suggest otherwise is misleading and reflects a reductionist narrative that prioritizes market positioning over technical accuracy.

Growth strategies that narrow the scope of condition monitoring do not improve reliability; rather, they dilute it.

As industry leaders, we have a responsibility to represent these technologies accurately and with integrity, ensuring that end users are equipped with the full range of tools necessary to detect, diagnose, and prevent failure. Ultimately, anything less is a disservice to the profession and the organizations that depend on it.

References

1. ISO. (2018). ISO 17359: Condition monitoring and diagnostics of machines—General guidelines.
2. Moubray, J. (1997). Reliability-Centered Maintenance.
3. Bloch, H. P., & Geitner, F. K. (2014). Machinery Failure Analysis and Troubleshooting.
4. Stachowiak, G. W., & Batchelor, A. W. (2014). Engineering Tribology.
5. Totten, G. E. (2006). Handbook of Lubrication and Tribology.
6. Macian, V., et al. (2003). Wear, 255, 1297–1305.
7. ASTM International. (2020). Standards for used oil analysis and condition monitoring.
8. Hutchings, I. M., & Shipway, P. (2017). Tribology: Friction and Wear of Engineering Materials.
9. Anderson, D. (2012). Oil Analysis Solutions.

Several years ago, I published an article titled, “How to Get Started with Onsite Oil Analysis: A Step-by-Step Guide”.  In that article, I outline 7 steps to developing a successful onsite program:

1. Developing an equipment criticality profile
2. Determining sampling frequency
3. Developing a sample test slate and alarms
4. Designing a lab
5. Designating a lubricant storage space (and keeping it tidy and clean)
6. Training
7. Software

Let’s build on that article and review some key advancements in the industry that are highlighted in Spectro Scientific’s TruVu 360^TM software and the new AI-enabled oil health forecasting tool, TruVu 360^TM Fluid IQ.  We will look at how the new features in the software can help with defining equipment criticality and risk, sampling frequency, and oil analysis alarms.

Equipment Criticality

Defining equipment criticality remains the first step in getting started with managing a program onsite.  There are several standard methods the end user can use to evaluate criticality, including ASTM 7874, Standard Guide for Applying Failure Mode and Effect Analysis (FMEA) to In-Service Lubricant Testing, and ASTM D6224, Standard Practice for In-Service Monitoring of Lubricating Oil for Auxiliary Power Plant Equipment.

Once criticality is determined, reliability engineers need to use that information and incorporate it into the overall workflow of the oil condition monitoring program. That means prioritizing maintenance checks, sampling, and testing based on the risk profile.

In TruVu 360^TM, the equipment risk profile is defined using concepts from ISO 13381-2025.  Users can assign these risk levels within the TruVu 360^TM software, with TruVu 360TM Fluid IQ enabled, to each component.  Risk and recommendations from the software go hand in hand.  The greater the risk associated with the component, the more conservative the recommendations for sampling and oil changes.  An important concept to define early and evaluate often to ensure the program continues to meet the organization’s goals.

Sampling Frequency

Determining the sampling frequency for critical components is often the most complex part of setting up a program.  Reliability engineers can find themselves oversampling, but more commonly, not enough.  Relying on industry documentation and OEM’s has been the norm, but new advances in artificial intelligence have enabled smarter sampling strategies.

TruVu 360^TM Fluid IQ users now have the opportunity to implement smart sampling into their maintenance strategies. Building on the idea of risk evaluation, sampling recommendations are made using sampling history and historical trends, and comparing to a broad database of like-components, operating history, and sample history to forecast when the next sample needs to be taken. This strategy enables optimized sampling aligned with the condition rather than fixed schedules.

Alarms

Setting proper alarm levels is also a challenging part of managing an onsite program.  Again, OEM recommendations, ASTM standards, and reference materials are available to help.

Users may find the ideas outlined in ASTM D7720 helpful (ASTM D7720: Standard Guide for Statistically Evaluating Measurand Alarm Limits when Using Oil Analysis to Monitor Equipment and Oil for Fitness and Contamination).  ASTM D7720 outlines condition-based alarm concepts, helping users adjust alarm levels based on the component’s condition.

This methodology is particularly helpful for users who may have a large number of severe alarms to manage (and reduce) but simply can’t address everything at once.  The statistical models referenced in D7720 enable alarm adjustments based on historical data to effectively identify extremely elevated alarms and address maintenance concerns promptly.

By employing this approach as a systematic process, prioritizing and resolving the most significant alarms first, then reassessing after each stage, it is possible to incrementally return all alarms to their normal status.  When utilizing this technique, it is important to use the equipment criticality profile, which is directly correlated with safety, and ensure that adjusting alarms to support condition-based alarming, as outlined in ASTM D7720, is appropriate.

This condition-based alarming (ASTM D7720) concept is implemented in TruVu 360TM and can be easily applied if sufficient oil sample history is available for the component.

Software

There is software for every function of life, personal or work.  It can be overwhelming at times.  The key is to purchase software that offers the most options for quickly and effectively implementing an oil condition monitoring program onsite.  Spectro Scientific’s MiniLab with TruVu 360TM solution is a valuable tool for a complete, all-in-one oil analysis solution onsite.

With the new AI-enabled forecasting tool within TruVu 360^TM users can:

• Evaluate and record criticality of components using risk profiles
• Utilize AI to optimize sampling frequency
• Create condition-based alarms using ASTM D7720
• Utilize AI to predict the remaining useful life of the oil
• Understand limiting properties and catch issues early (even when the oil condition is normal)

Conclusion

It’s exciting to be part of the industry’s integration of AI techniques into onsite lab workflows. While the core principles of condition monitoring programs remain relevant, they are adapting to include advancements in AI.  If you would like more information about onsite oil analysis solutions, please reach out to me.

Learn More about TruVu 360 Fluid IQ

Oils are composed of base oils and additives. Typically, additives are sacrificial; they deplete first before the base oil is affected. As such, by trending their quantities over time, we can gain insight into a few of the conditions to which the oil is subjected.

By interpreting these conditions and patterns, we can correlate them with the health of the asset and plan accordingly for possible repairs or maintenance. In this article, we will do a deeper dive into ways these can be explored to add value to your asset management program.

Why Do Additives Matter?

Additives come in various ratios and chemical compositions, but when we talk about additives in oils, they really have three main functions. They can either;

• Enhance the properties of the base oil, which already exist
• Suppress the undesirable base oil properties or
• Impart new properties to the base oil

On their own, they cannot affect anything, but when coupled with a base oil, they can impact an asset. Base oils also have specific properties, which, when combined with additives, allow assets to perform at their best.

The real performance comes from how the additives and base oil work together.

As shown in Figure 1, some additives that enhance properties include antioxidants, corrosion inhibitors, anti-foam agents, and demulsifying agents. Those responsible for suppressing undesirable properties can include pour-point depressants and viscosity improvers.

Finally, those responsible for imparting new properties include extreme-pressure additives, detergents, metal deactivators, and tackiness agents.

Here are some quick descriptions for a few of these additives, which will help you to gain an appreciation of their functions:

Antioxidants: these protect the oil from oxidation. They are very common in Turbine oils but can be found in many other oils. They are the first line of defense when oxidation begins and react with free radicals to neutralize them before they attack the base oil. 

Corrosion inhibitors: adsorb onto the metal surfaces to help protect them. Comprised of sodium sulphonates, alkylbenzene sulphonates, or alkylphosphoric acid partial esters.

Anti-foam agents: reduce surface tension to break up foam formation. Typically, these are silicon-based, although silicone-free defoamers also exist.

Demulsifying agents: enable water and oil to separate. These were formerly composed of barium and calcium, but modern formulations use special polyethylene glycols.

Pour point depressant: alters oil crystallization, allowing the oil to form fewer crystals at lower temperatures.   

Viscosity improvers: specifically designed to ensure that the viscosity of the lubricant can be more tolerant of changes in temperature and shear.  

Extreme pressure additives: used under high stress to prevent the welding of moving parts. Usually comprised of a phosphorus compound.

Antiwear additives: designed to reduce wear under moderate stress. The most famous sulphur-phosphorus compound is ZDDP (Zinc Dialkyl dithiophosphate).

Detergents: keep oil soluble combustion products in suspension (especially for engine oils) and ensure they do not agglomerate. These usually contain metal additives such as Calcium and Magnesium.   

Understanding the function and composition of these additives can help us to determine how they are performing in the oil. Since many of these are sacrificial, their values will decrease over time. As such, it is important to trend these values to determine whether they are remaining constant, increasing, decreasing, or decreasing at an accelerated rate.

How Can Additives Deplete?

Additives can be depleted through different mechanisms. Some of these include:

• Regular consumption through normal functioning of the lubricant
• Antioxidant depletion during oxidation
• Antiwear depletion due to high wear on the inside of the equipment
• Additive depletion via a contaminant to produce a bleaching effect

As mentioned earlier, additives are sacrificial in nature. It is very normal to see additives deplete over time; if they are not depleting and increasing, this may be a cause for concern. This can mean that someone is topping up the oil frequently or perhaps topping up with an incorrect lubricant.

Since there are numerous oils on the market, the best way to monitor the depletion of your additives is to compare them against a new sample of that oil and use that as your baseline. Your lab will help you confirm when the additive limits are approaching the danger zones.

During oxidation, a free radical is formed under conditions such as heat, wear, metal catalysts, oxygen, or water. These free radicals are unstable, and antioxidants usually neutralize them.

In the process, antioxidant levels decrease. However, if the conditions still permit oxidation, more free radicals will be formed. This means that more antioxidants will be depleted as they neutralize the free radicals until they diminish and can no longer protect the base oil. This is when the free radicals begin to attack the base oil, and varnish can form.  

Once the antioxidants are gone, the oil stops defending – and starts degrading.

If there are causes of high wear, such as the incorrect viscosity of the lubricant (too thin) or the machine finishing of the inner parts of a component not being done to the required standard, this can affect the levels of antiwear in the oil. Antiwear additives protect the metal surfaces inside the equipment. However, these are only activated when moderate stress exists within the equipment.

Typically, in these situations, the antiwear additive adheres to the metal surface and helps protect it by forming a layer. Once this layer is formed, the antiwear additive has officially left the oil, and this will be reflected in a decrease in its value in the oil analysis report.

The layer will not remain forever, and due to wear on the equipment, it can be worn off and replaced by a new layer, leading to further depletion of the antiwear additives until there are no more to form another layer or protect the metal surface.

Contamination can also cause some additives to become depleted. Contaminants can react with additives, causing them to form deposits that leave the oil. Therefore, their presence will not be detected by oil analysis.

Some common contaminants are water, fuel, coolant, and acids. These contaminants can also promote the formation of catalysts for degradation mechanisms such as oxidation. Dirt and solid particles can also promote additive depletion, especially when they act as catalysts. 

What Tools Can Be Used to Monitor Additive Depletion?

There are some basic analytical tools that can be used to measure the quantity of additives in oils. The spectroscopy methods are the FTIR (Fourier Transform Infrared) and ICP (Inductively Coupled Plasma). Another method is the RULER® test exclusively designed for antioxidants.

With FTIR and ICP methods, users obtain quantitative values for the elements present in the tested oil sample. These are usually reported in ppm and trended over time. Figure 2 shows an extract from a Turbine Sample report from Eurofins lab, where the levels of additives (and contaminants) are shown. When trending this, analysts should pay attention to the rate at which these additives decrease and whether an increase or decrease is noticed.

Another tool that can be used is the RULER® (Remaining Useful Life Evaluation Routine) test, which specifically quantifies the levels of antioxidants remaining in the oil. It trends the values, compares them against the baseline for that oil, and then determines the change as a percentage.

If the RULER value falls below 25%, the antioxidant levels have reached a critical level, and one may consider replacing the oil. 

Figure 3 shows a RULER graph, which identifies the presence of different types of antioxidants (Amines) and antiwear additives (ZDDP), as well as oxidation products (Fluitec, 2022).

This is a comprehensive readout of the quantities of these additive types at the time of sampling. It is easy to get a quick snapshot of its trend over time and determine whether it is declining rapidly or reaching critical levels.

The key to using these analytical tools is to provide insight into what is happening inside the equipment, allowing us to determine whether any preventive action is needed.

By monitoring the quantities of these additives over time, we can easily establish whether oxidation is occurring, which can lead to varnish and overheating of the asset. We can also determine whether significant wear is occurring as the antiwear additives are depleted (confirmed by the presence of wear metals in the oil analysis). When monitoring your asset’s health, trending specific additive levels can also be very useful. 

References

Eurofins. (2025, September 06). Annual Turbine Analysis. Retrieved from Eurofins Testoil: https://testoil.com/services/turbine-oil-analysis/annual-turbine-analysis/

Fluitec. (2022, September 29). Why is LSV Used for RULER Analysis? Retrieved from Fluitec: https://www.fluitec.com/2022/09/29/why-is-lsv-used-for-ruler-analysis/

The EGR (Exhaust Gas Recirculation) system recirculates a fraction of the exhaust gases into the intake manifold to lower combustion temperature, reduce NOx emissions, and, in some cases, improve fuel consumption in both diesel and gasoline engines. This recirculation of hot gases introduces more soot, acids, and contaminants into the lubricant, accelerating its degradation and reducing engine life if not controlled with proper design, oil, and maintenance.

In diesel engines, the EGR valve diverts a portion of the exhaust gases before or after the turbocharger. It reintroduces them, often after cooling in an exchanger, into the intake to reduce effective oxygen and flame temperatures, thereby decreasing NOx formation.

The valve is governed by the ECU (Engine Control Unit) based on load, temperature, and rpm, opening primarily at partial loads and closing at cold idle or full load.

In gasoline, EGR can be hot or cooled. It is used to reduce part-load pumping losses and to lower NOx emissions. By recirculating inert gas, the valve can be opened wider at the same torque, reducing intake vacuum and improving efficiency. In GDI (Gasoline Direct Injection) and turbo engines, external or internal EGR (through valve overlap) is adopted to control combustion temperature and mitigate knock.

The latter refers to reducing the mixture’s tendency to detonate spontaneously before or just after the spark, a common problem in high-compression gasoline engines. Recirculating inert gases lowers the combustion temperature and slows the reaction rate, reducing the likelihood of knocking and allowing greater advance or load without damaging the engine.

Typical EGR Failure Modes

No precise statistics indicate exact percentages of EGR valve failure modes. However, there are two failure modes that, depending on the engine type (diesel or gasoline) and the OEM, account for a high percentage of total failures. (1)

Open seized EGR valve ~ 65%

Excess recirculated gases, loss of power, smoke, possible increase of soot in the manifold and in the combustion chambers of the engine cylinders.

When the EGR valve is seized open, it recirculates excess exhaust gases (with soot and contaminants) into these chambers, diluting the fresh mixture too much, reducing power, and causing incomplete combustion that generates more soot accumulated on the walls of the chambers, pistons, valves, and intake manifold.

Excess soot in combustion chambers tends to raise temperatures, increase wear, and complicate gas flow in subsequent cycles, aggravating failures such as compression loss or excessive emissions.

In open-valve failure, the vicious cycle of soot obstructing the intake reduces airflow, forcing more fuel to compensate, increasing consumption and emissions. In contrast, NOx is reduced by excessive dilution. In diesel engines, it triggers frequent DPF regeneration due to low soot levels.

DPF regeneration is the automatic or manual cleaning process of the Diesel Particle Filter, which burns the soot accumulated in its ceramic channels to prevent clogging and keep emissions low, converting carbon particles into CO₂ and ash at temperatures of 500-600°C.

Closed seized EGR valve ~ 15%

High NOx, possible detonation, higher temperatures, and risk of thermal damage to metal components.

In a closed-valve failure, the ECU detects high NOx and enters safety mode (reduces power, limits rpm), accelerates piston thermal wear, and saturates the DPF due to insufficient dilution. In this failure mode, lubricating life degrades dramatically, reducing TBN at an accelerated rate.

In some post-2021 diesel engines, or Boxer-type engines with high-pressure EGR, a mixture of soot and light-fraction oil vapors from the combustion chamber forms dense sludge in the manifold, valve, and cooler, thereby aggravating EGR-system failures.

EGR failure identification opened by oil analysis

The failure mode of EGR valve seized open, excess recirculation introduces high volume of soot (carbon particles) and acidic contaminants (SOx, NOx dissolved) to the crankcase via blow-by, drastically raising the soot content (>2-5% wt), increasing viscosity and TAN (total acid number), and lowering TBN (total basic number) in an accelerated manner. This indicates incomplete combustion and dirt in intake – combustion chambers; oil detergents-dispersants become saturated, forming sludge and accelerating abrasive wear (Fe, Al, Pb high in iron, aluminum).

Closed EGR failure identification by oil analysis

In the closed-seized EGR valve failure mode, combustion is hotter. It generates greater oxidation/nitration of the oil, reflected in a high oxidation index (IR spectrum >30 Abs/cm), an increase in viscosity is evidenced by polymerization, and TBN is depleted by neutralization of the excessive load of NOx acids.

On the other hand, rising temperatures cause thermal wear due to thermal expansion. Increasing wear is measured by metal loss (chromium and iron from bearings/pistons). A low soot concentration (<2%) may be observed, possibly due to ECU injection adjustments that increase fuel dilution. In some cases, this dilution can cause the absence of soot despite high temperatures.

Global market for the sale of used and new cars

There is no accurate global data for annual sales of new and used cars worldwide. On the other hand, used markets vary significantly by region and country, and there is no unified source like OICA for used. But the data shows a worldwide trend where the sales of used cars exceed new cars by at least 3:1, according to the estimated data:

According to the data, it is very likely that, at some point, you will have to buy a used car; specifically, there is an 80% chance (equivalent probability) that you will do so.

At the same time, you’ve likely never done an oil analysis on your car. But if you’ve paid some attention to this article, chances are you’ve understood the value of a simple oil analysis—an analysis with a cost of nearly $30.

Over the years, I have helped several friends choose a second-hand car. Where the exterior can be very presentable, clean and very well maintained; But inside, where many times not even today’s computers can identify the problem unless it is very advanced states, a simple analysis of the oil in service can be the difference between buying a problem or making sure that the car’s engine – the heart of the machine – is working properly.

Understanding Electrostatic Spark Discharge and Its Impact on Lubrication Systems

Electrostatic Spark Discharge typically occurs when static is built up in an oil at a molecular level, causing it to discharge in the system and create free radicals, which increase the opportunity for varnish to form. This usually occurs at temperatures of around 10,000 °C.

If we were to liken this to an everyday situation, we could think about walking around a carpeted room where the static builds up in our body. When we touch a metallic object (more than likely a door handle), we get a bit of a shock as the built-up static is discharged through us and the door handle.

Inside a lubricant system, static doesn’t just build – it ignites microscopic sparks powerful enough to scar filters and start the chain reaction that leads to varnish.

Similarly, in lubricants, static exists at a molecular level, and in areas of tighter clearances, some molecules are forced to rub against each other, causing a buildup of static. When it accumulates to the point of becoming a full charge, it dissipates at the first opportunity, usually at the filter membrane or some sharp-edged object along the way. These are seen as burnt patches on the filter membrane.

When this spark occurs, it creates a chemical reaction that generates free radicals. Free radicals are highly reactive species that need to engage with other substances. These are the initiators of varnish, and their presence can accelerate reactions, leading to deposits forming in the lubricant. Eventually, this will lead to a system that has experienced both ESD and oxidation.

In this article, we will discuss various identification methods and ways to prevent ESD in modern lubrication systems. We will also spend some time identifying typical root causes for ESD by developing a logic tree as a guide for future investigations.

How to Identify Electrostatic Spark Discharge in Lubrication Systems

Every degradation mechanism produces varying results in the form of deposits or in how these are formed. For ESD, some tell-tale signs alert the user to its occurrence. These include:

• Crackling sounds / buzzing outside of components – This noise is representative of sparks as they discharge on part of the media/asset. Typically, this occurs when the fluid is in motion, allowing it to be heard near the filters when the system is operating.  
• Burnt or pinhole filter membrane – The filters usually feel the full effect of ESD, and small burn patches or even pinholes are created when ESD occurs. When changing filters, the membranes should be examined for these patches to determine if ESD is occurring.

Free radicals are produced when ESD occurs. As such, this leads to polymerization of the lubricant, which produces varnish and sludge. This is part of the oxidation process, and the antioxidant levels will begin to decrease. During ESD, certain gases are also released in the oil. Some of the lab tests which can be used for identifying where ESD has occurred include:

• RULER® – Remaining Useful Life Evaluation Routine test, which quantifies the presence of antioxidants in the oil. By trending this over time, one will be able to determine whether the levels of antioxidants are decreasing or not. Typically, this test can be performed twice annually on larger sumps (such as turbines) or the frequency can be increased according to the criticality of the equipment. If the value gets below 25% then this is the critical limit, and methods to regenerate the oil or change it should be explored.
• MPC – Membrane Patch Colorimetry – this measures the potential of the oil to form varnish or deposits. Depending on the equipment, the warning limits will vary, but a good rule of thumb is to treat results below 10 as normal, those above 15 as within the monitor range, and those above 20-25 as the critical range. Be sure to double-check these levels with the OEM of the equipment.
• FTIR – Fourier Transform Infrared Spectroscopy can identify various molecules in the oil. It is likened to identifying the fingerprint of the oil, where each molecule has a specific characteristic spectra representative of that molecule. This test can be used to identify the presence of oxidation or any deposits that may have formed.
• DGA – Dissolved Gas Analysis – this test can be used to identify the presence of particular gases that are released during ESD, such as acetylene, ethylene, and methane.

Those above are just some of the methods that can be used to identify the presence of ESD in a lubrication system.

Effective Strategies to Prevent ESD in Lubrication

ESD occurs when there is a buildup of static in the oil; therefore, one of the best methods of preventing it is to ensure that the static levels remain low or are dissipated before they have a chance to wreak havoc on the system. The simplest and most common way of reducing this static is the installation of antistatic filters. These filters can help to remove static from the system before it builds up to dangerous levels, where it can burn the membranes or develop varnish.

Static in oil is inevitable – how you control and discharge it determines whether your system runs clean or burns itself from within.

Ensuring that the system is grounded correctly is another way to guarantee that any built-up static is removed. This is where your electricians can perform checks and install proper grounding devices for your equipment to safeguard against this buildup of static in the system. Therefore, if static charges get built up in the system, they can be dissipated without the effects of ESD.

If oils experience high levels of conductivity, they can conduct static. Typically, if the conductivity is above 100pS/m, there is potential for the fluid to conduct the charge and allow it to be discharged along the system without causing harm.

Unfortunately, there are base oils with low conductivity (below 100pS/m) that cannot carry the charge and dissipate more easily. As such, these types of oils will see an increase in the presence of ESD if not formulated correctly for modern lubrication systems.

As the viscosity of the oil decreases, more force is required to pass through the filters, which can lead to a buildup of static at a molecular level. Additionally, as temperatures decrease, the viscosity also decreases. In these cases, keeping the oil at the system temperature (designed for that particular viscosity) can help to reduce the buildup of static charge in the oil.     

Identifying the Root Causes

Thus far, all the prevention methods have focused on the physical roots of ESD. We did not explore some of the human or systemic roots that are also accountable for ESD. In this section, we will develop a logic tree designed to address a critical failure occurring in a plant. This will be used as an example of the logic tree, which can be developed when investigating the root causes of ESD.

Let’s start with the top of the logic tree, where we define the event or the reason we care. In this situation, it is an unplanned shutdown for 4 hours. An unplanned shutdown will impact the plant’s production, which is why we care about conducting this investigation.

We will assume that the failure mode occurred on one of the critical pumps, and we are investigating how that failure could have happened. Note, we did not ask the question “Why?” because it can be misleading to an opinion, and we are trying to stay as factual as possible.

A disciplined root cause analysis doesn’t start with ‘why’ – it starts with evidence. Each degradation mechanism tells its own story, and Electrostatic Spark Discharge is just one of them.

For this investigation, we will investigate the hypothesis of a critical bearing failing due to lubricant degradation. Since we are focused on ESD for this article, we will have only one hypothesis regarding the degradation mode being ESD. However, in the real world, if this logic tree were being developed, we would be investigating the six various lubricant degradation mechanisms: oxidation, thermal degradation, microdieseling, ESD, additive depletion, and contamination.

As shown in Figure 1, at the top of the logic tree, we start by placing our hypotheses on the tree, and then using evidence/facts, we can rule them out afterwards. This is a critical step as the investigation should be able to stand on its own in the court of law (even if it may not reach that point). The next hypothesis is the buildup of static in the oil. There are three possible ways for this to occur;

• Clearances too tight (as discussed earlier, this can lead to molecular friction, which can induce static)
• Less than adequate grounding system (if a proper grounding system doesn’t exist, then there isn’t an option for the static to dissipate)
• Less than adequate conductivity of the oil (if the conductivity of the oil is too low, then the charge can build up and cause it to be dissipated along the system, such as the filters)

Now, we need to investigate each of the three main hypotheses stated and find out the root causes for them.

Let’s begin with the Clearances being too tight hypothesis.

For this hypothesis, we will ask the question, “How can clearances be too tight?”. In this case (and we will have to keep it general and in broad buckets, so we can drill down into these later and eliminate as necessary), there are three possible reasons:

• The OEM could have designed the system such that it was less than adequate (LTA)
• The flow rate could have been increased above the recommended threshold
• Incorrect viscosity of the lubricant could cause additional friction (we will dive into this one later).

We will develop the other two hypotheses in Figure 2. 

If we further investigate where the OEM did not design the system effectively, we can determine that the operating conditions were probably not adequately considered before implementation. This is a systemic root cause and one that needs to be addressed with the OEM.

On the other hand, if we investigated the flow rate, there could be two main reasons for the adjustment. One could be because of system changes, which forced adjustments to the flow rate. Since a decision was made here, it is a human root cause. Someone decided to change the flow rate based on the variables involved. However, if we investigate why these changes were made (once a human is involved, the question moves from how to why), we can determine that the system was not designed to accommodate these changes. This is a systemic root. 

Similarly, if the operational conditions change (such as when a higher output is required, which is different from system changes), then the flow rate must be adjusted. Again, a decision must be made here, and a human is involved. Then we ask the question, “Why?”. In this case, we have the same systemic root, and the design is inadequate to accommodate the necessary changes.

For this part of the tree, we have found some human root causes where decisions were made, as well as systemic root causes. Both need to be addressed when we perform the final root cause analysis. For the human root causes, we can think about the procedures that guided them to make those decisions (if they existed) and amend these accordingly. 

On to the next hypothesis, which we have yet to investigate (still under the clearances being too tight), the incorrect viscosity of the lubricant, which is shown in Figure 3. There are a couple of ways in which this can happen:

• OEM recommendations were not followed
• There was an unavailability of the specified viscosity of the lubricant
• There was a less-than-adequate procedure for selecting the correct viscosity of lubricant

If we investigate why the OEM recommendations were not followed, we can find two main reasons. Either they were not documented and therefore could not be followed, or the internal best practice was used instead to replace the OEM recommendations. In both cases, these would be systemic root causes, and we should investigate why these were not documented or why they were replaced.

When investigating the unavailability of the specified viscosity of the lubricant, we can find two main causes. Either there was an issue with the restocking of this lubricant at the warehouse due to their forecasting, or appropriate checks were not carried out. This is a systemic root cause that should be investigated further.

Another hypothesis could be that the specified lubricant was unavailable from the supplier. This is another systemic root cause and should be addressed with the supplier to ensure it is resolved in the future. 

When lubricant viscosity errors trace back to missing stock or missing training, the problem isn’t the person or the product – it’s the system that allowed both to fail.

On the other hand, if we examine the procedure for selecting the correct viscosity of the lubricant, we identify a human root cause, as someone would have made the decision on which viscosity to use. But in this case, we need to investigate why the person was not trained to determine this value.

There are two main reasons why a person does not receive training: either it doesn’t exist, or it was not followed. In both cases, these are systemic roots that need to be further investigated and addressed.

Now, we will investigate the next major hypothesis, “LTA grounding of the system” in Figure 4.

When investigating the grounding of a system, we can identify two major classes: either it doesn’t exist, or it didn’t meet the requirements. If grounding did not exist, then this is an inadequate system design and therefore a systemic root cause. On the other hand, if the grounding did not meet the OEM requirements, we need to determine how this was possible.

There are two possibilities: the site’s best practices were used to replace the OEM standards, which is something we often see, especially if these requirements have worked in the past. This is a systemic root cause that should be investigated. Or there were fewer than adequate components to achieve grounding.

In this case, we can have components that are not designed for the system (do not meet the system’s requirements) or components that were not OEM-recommended and are being used (such as aftermarket products that do not meet the necessary specifications).

Finally, on to the last major hypothesis, “LTA conductivity of the oil,” as shown in Figure 5.

Figure 5: Investigation of the hypothesis, “LTA Conductivity of oil”

As noted earlier, if an oil has a conductivity of more than 100pS/m, it will be able to dissipate any accumulated charge easily. However, if it falls below this value, the charge will be dissipated in the system at the earliest opportunity.

How can oil have less than adequate conductivity? Perhaps the elements of the oil have a less-than-adequate conductivity. If that is the case, then there can be two plausible reasons for this. Either the formulation was not appropriately designed, or the materials (base oils, additives) were not of a particular standard. Both causes are systemic root causes and should be investigated further to determine if anything can be done to correct these. 

If we were to summarize a list of the root causes, we would see that many are systemic, while a few are human, as shown in Figure 6.

This further reiterates the need to develop a comprehensive logic tree when investigating any failure, as many root causes are not physical or surface-level. If these are not adequately addressed, the failure mode will recur in the future. The entire logic tree can be found here under additional support material, along with logic trees for other degradation mechanisms.

References

Mathura, S. (2020). Lubrication Degradation Mechanisms: A Complete Guide. Boca Raton: CRC Press.

Mathura, S., & Latino, R. (2021). Lubrication Degradation: Getting into the Root Causes. Boca Raton: CRC Press.

Lubrication analysis plays a vital role in predictive and preventive maintenance for diesel engines. Among the most informative tests available are Acid Number (AN) and Base Number (BN), two complementary measurements that provide insight into oil health, additive performance, and the potential for damaging conditions inside the engine.

The true picture of oil health emerges only when acid number and base number are analyzed together.

While historically BN has been the primary focus in diesel applications, changing lubricant formulations and emissions-driven oil designs have made AN testing equally critical. Together, AN and BN monitoring support condition-based maintenance strategies, reduce unplanned downtime, and optimize oil drain intervals.

Understanding the Fundamentals of Acid Number and Base Number

Acid Number (AN) measures the amount of acidic compounds in a lubricant. Acids can form from oxidation, nitration, or sulfur byproducts of combustion, and they can be highly corrosive to internal components if left unchecked. A rising AN signal indicates degradation and potential corrosive wear.

Base Number (BN) measures the alkaline reserve in an oil or the capacity of additives (primarily detergents) to neutralize acids formed during engine operation. As the oil ages and these additives are consumed, the BN value declines.

Both parameters are critical for oil condition monitoring: BN tracks how much “protection” against acids remains, while AN indicates how much corrosive material has already accumulated, and is the preferred test due to its ability to measure at lower levels, more on this later in the article.

The Role of Acid Number and Base Number in Diesel Engine Performance

In diesel applications, the balance between AN and BN provides a powerful indicator of lubricant condition. A healthy oil will show a gradually decreasing BN alongside a relatively stable, slowly increasing AN. When BN drops too low, acids are left unneutralized; when AN rises too high, corrosive attack accelerates.

Modern low-ash diesel oils have changed the rules – tracking both acid and base numbers is now essential for accuracy.

The challenge in today’s engines is that newer low-SAPS (sulfated ash, phosphorus, sulfur) formulations (introduced to prevent plugged diesel particulate filters (DPFs) and EGR failures) start with lower BN values and of`ten deplete more slowly. Some products are designed for emissions compliance and reduced ash formation due to low starting base numbers. As a result, relying solely on BN alone can be misleading.

In these cases, monitoring AN is vital. Even while BN remains relatively stable, AN can increase due to oxidation, fuel dilution, or coolant leaks. This dual tracking is why an increasing number of operators in Europe and globally now perform both AN and BN testing on diesel engine oil samples.

Impact on Oil Drain Decisions

One of the most practical applications of AN and BN analysis is in determining oil drain intervals. Traditional time- or mileage-based drain schedules often result in premature oil changes (waste) or delayed changes (risk of damage). Condition-based monitoring, by contrast, uses AN and BN data to identify the oil’s true remaining useful life.

AN does not increase linearly. Instead, it follows a curve: a slow, gradual rise at first, followed by a sharp increase once antioxidants are depleted. This inflection point represents the end of the oil’s life, when oxidation accelerates and corrosive wear risk rises rapidly. Effective condition-based maintenance requires identifying this inflection point and scheduling oil changes 10–20% before the oil reaches it.

By trending BN depletion alongside AN increase, maintenance teams can strike the right balance and extend drains safely while avoiding the steep escalation of wear and damage.

Standard Test Methods for Measuring Acid Number and Base Number

Several ASTM methods are commonly used for AN and BN measurement:

• ASTM D2896 (BN) – Uses a potentiometric titration with perchloric acid; highly sensitive but can give elevated values in used oils. (This test is designed for new oil only and should not be used for used oil, as the acid used in the titration will react with wear metals in used oils)
• ASTM D4739 (BN) – Uses hydrochloric acid; considered more representative of remaining alkalinity in used oils
• ASTM D664 (AN) – Potentiometric titration with KOH, used to determine acidic constituents in petroleum products
• All test methods report mg KOH/g

The choice of method depends on oil formulation, application, and whether the sample is new or in service. Many labs report both D2896 and D4739 for BN to give operators a complete picture. However, D2896 is not recommended as it is not a suitable method for new oils; most labs in Europe do not perform D4739 and use a modified method (different solvent) for D2896.

As modern diesel oils begin with lower base numbers, traditional test methods lose precision – making careful method selection critical.

BN was designed for fluids with a starting BN of ~ 12 mg KOH/g. Now they have a starting value of about 6 mg KOH/g. Neither method for measuring BN is accurate at lower levels.

On the other hand, AN was designed for lower levels (>4 mg KOH/g) and gives a better discrimination of the new fluids as they age. AN is a titration with potassium hydroxide (KOH). The KOH reacts mainly with carboxylic acids.

Carboxylic acids are degradation products caused by the oxidation of lubricating oil and additives. Thus, AN, in conjunction with FTIR (Fourier-transform Infrared) oxidation, gives an excellent definition of the oxidative state of a lubricating fluid.

Engine Damage Risks from Imbalanced Acid and Base Numbers

If AN and BN trends are ignored, several damaging conditions can occur inside a diesel engine:

• Corrosion of bearings and liners from acidic byproducts attacking metal surfaces
• Ring and cylinder wear from both acidic attack and ash formation when BN is insufficient
• Accelerated wear and deposits from oxidation products, sludge, and varnish as AN spikes
• Reduced reliability and costly downtime due to preventable mechanical failures

Finding the Critical Balance

As diesel engine oils evolve to meet emissions and performance requirements, maintenance practices must adapt. Relying on BN alone is no longer sufficient, particularly with low-SAPS, ultra-low-ash formulations. By monitoring both AN and BN, operators can accurately track oil condition, optimize drain intervals, and prevent corrosive wear.

Fluid analysis isn’t just about data; it’s about actionable insight. The balance between AN and BN is one of the most powerful tools in the maintenance toolkit, ensuring engines operate efficiently, safely, and for the long haul.

What is Condition Monitoring and Why Is It Important?

In this age of artificial Intelligence and sensors that pop on and off, we often forget about the basics and where things all started. Condition monitoring began as a way to detect anomalies in our equipment using various types of technologies. These include: vibration, ultrasound, infrared, oil analysis, and even temperature.

These were all conditions that were “aligned” with what was happening on the inside of the machine. As such, changes in their values usually indicated that something was occurring, but it was up to the trained analyst to determine if that was a good thing or a bad thing.

The most effective reliability programs blend multiple condition monitoring technologies to catch failures before they happen.

For this article, we will focus heavily on oil analysis, but this does not mean that it’s the only technology that should be used for monitoring your equipment. It has been proven that a combination of technologies can maximize the opportunity to detect an impending failure earlier and allow the maintenance team to act/plan accordingly. This can save millions of dollars depending on the industry and the type of equipment.

Using Oil Analysis as a Core Condition Monitoring Tool

Stated, oil analysis can be any test performed on the oil that has been in use in the system. It is essential to note that the oil sampled should be representative of the system; otherwise, the results can lead to operators making inaccurate decisions.

For instance, oil taken from a dead leg of the equipment or in a stagnant zone does not truly represent the oil in the system. This can give a false representation of the system and cause misdiagnosis.

Depending on the equipment being monitored, specific tests would be required to determine the health of those systems. For example, with a turbine oil, one specific test would be the RULER® test to determine the remaining useful life (in the form of antioxidants).

However, if this test were performed for a transformer oil, it would not provide the operator with the necessary information, and more aligned tests such as Viscosity, Dissolved Gas Analysis, or Flash point would be more suitable.

Benefits of Extending Oil Drain Intervals

Before diving further into the condition monitoring aspect, we need to answer the question, “Are there any real benefits to extending the oil drain interval of a piece of equipment?” The answer depends on the criticality of the equipment and the cost associated with its downtime.

Financial Gains from Extended Oil Drain Intervals

For critical equipment where maintenance downtime hampers production or availability, extending oil drain intervals offers tremendous financial benefits. For every oil drain interval, there are associated costs such as manual labor, cost of supplies (filters, new charge of oil), and disposal of used oil, to name just a few.  

Every unnecessary oil change wastes labor, materials, and money that could be invested in reliability.

Depending on the size of the sump, costs can escalate, particularly if cleaning is required before the new oil charge is placed into the equipment. Different types of applications will advise the draining of the sump and refilling with new oil, while others recommend that the sump be flushed or manually cleaned before the new oil is administered.

Additionally, if the used oil becomes heavily contaminated during use, the sump and entire system would need to be cleaned thoroughly before new oil is used.  

Safety and Environmental Advantages of Extending Oil Drain Intervals

Apart from the financial benefit of extending the oil drain interval, there are also safety and environmental benefits. If these pieces of equipment are in high-risk areas, then the humans involved in changing the oil would be placed at risk during these times.

If the oil drain interval is extended, then the humans performing these operations will have reduced hours spent in these high-risk areas. As such, it will limit the number of risk-hours and possibly lower the LTI (Loss Time Injuries) or occurrence of any such safety incidents.

Fewer oil changes mean fewer hours in hazardous zones – and fewer chances for accidents.

Every time the oil is drained from the sump, it must be disposed of safely. Typically, worksites have a dedicated area in which the used oil is stored until it is collected by a disposal provider. Some providers may charge based on the volume they collect or the frequency at which they service their customers. However, the oil must still be disposed. With longer oil drain intervals, there is a reduced volume of used oil collected by these suppliers.

Additionally, longer oil drain intervals also impact the consumption of new oil for these systems. Therefore, equipment owners would likely see a decline in the volumes of oil purchased. This also translates to a saving on the environment as resources used to create new oil are also now reduced, or rather, the demand may be reduced overall.

Another benefit of extended oil drain intervals is that the equipment is available for a longer time. This can become critical in some jobs where the equipment is needed 24/7 or even for an emergency. The availability of equipment can also translate into the potential saving of a life (depending on the equipment).

Overall, there are financial, safety, and environmental benefits to extending the oil drain interval for equipment.  

Dangers of Pushing the Limits with Oil Drain Intervals

There is always a danger in pushing limits; that’s why limits exist. They serve as guardrails to ensure that things remain within the standard envelope. As it applies to oil analysis, there are some dangers if the limits are not addressed.

Typically, maintenance intervals are determined by the number of hours worked or the mileage of equipment. These guidelines were developed by OEMs (Original Equipment Manufacturers) based on lab and, in some cases, field tests. Usually, these limits are set with some tolerance for “marginal error,” where the oil may not be changed exactly at the specified interval. However, nobody states what those margins are or what tolerance limits can be used.

In these cases, the oil, whether it has reached the end of its useful life or not, is changed in an attempt to protect the equipment from failing in the future. Hence, OEMs always recommend staying within the limits, as those are what they can guarantee / warranty. Pushing the limits may mean getting in a bit of trouble with your OEM, and they may void your warranty. However, if the benefits outweigh their concerns, then it may be time to push those limits.

What Should be Tracked in Oil Analysis

Every type of equipment will have different tests that should be performed to monitor its health. We will break down a few common types and the associated basic and some specific oil analysis tests that should be performed.

Diesel/Gasoline Engines (can be further broken down into on-road, stationary, aviation, landfill, and marine)

Basic (monthly tests)

Gearboxes (can be broken down into industrial or automotive)

Hydraulics

Turbines and Compressors

We did not dive into Electrical oils, heat transfer oil, circulating oils, metalworking fluids, or seal oils, but these will have similar type tests and some special tests as well.

Setting Up Baselines

Global oil suppliers have baseline or tolerance limits that are used when providing guidelines to customers about their equipment. The limits for a gearbox will differ from those of an engine. For instance, an iron content level of 3000ppm is normal for an automatic transmission gearbox but highly irregular for a diesel engine! Hence, it is important to know the limits associated with the application.

Some labs have also developed their own set of limits based on years of collecting hundreds of samples and liaising with their customers in the field. OEMs have also developed their own sets of limits (usually displayed in their manuals) based on their testing in the lab and on the field.

Knowing your own “normal” is more valuable than any generic industry limit.

Ideally, when developing your target levels, you should trend your data and find out what “normal” looks like for your equipment. In some cases, what is normal for your environment may be abnormal in a different environment. But it is important to note when normal varies from standard operating tolerances. This is where you would want to work together with your oil supplier, lab, and OEM to develop tolerances that align with your equipment.

Depending on your maintenance program, you can also adjust the tolerance accordingly. If you are aware that maintenance may not act on a threshold limit right away, it may be a good idea to add some padding to those limits. This ensures that the equipment does not suffer by pushing it to the limits.

Setting Oil Analysis Limits for Diesel Fleets

Let’s explore how to set the limits for a diesel engine fleet of trucks.

First, let’s categorize the trucks into critical, semi-critical, and non-critical.

The critical ones are those that, if they break down, there is no replacement; the downtime hurts us financially and can delay the project. These need to be available 24/7.

The semi-critical ones are those that still have an impact on the operation if they break down, but it’s not quite as disastrous. These can be trucks that are not on tight deadlines, can afford to have some leniency or delays with their workload.

The non-critical trucks are those that can be easily swapped out for another truck without causing any delay or impact to the project, but they are still important.

Now that these are categorized, we need to find out what types of engines are being used and what the recommended diesel engine oils are for these units. Typically, most operators have mixed fleets. Thus, one may see a wide age/mileage gap in the engines. This gives us an idea of the reliability of the engines, which can impact the setting of the tolerance limits.

Since it’s a diesel engine fleet, it would be worthwhile to consider the type of fuel being used for this fleet. With diesel engines, there are varying levels of sulphur in the fuel, which can impact the oil drain intervals as well.

For this fleet, we may need to establish varying oil drain intervals to ensure maximum reliability, based on the categories outlined by their criticality. Before adopting set oil drain intervals, it is important to execute a pilot project with the fleet to anticipate any rollout challenges for the future. We will discuss these in more detail in the case study section.

Real-World Results from a Diesel Fleet Oil Analysis Program

Fleet: Mixed long-haul trucks of various ages/mileages

Predominant oil: Mineral 15w40 Diesel engine oil (CI4 spec)

Regular Oil Drain Interval: 3000km (based on best practice over time)

Approach: An engine asset list was first compiled for every truck in the fleet. This follows the table below:

It’s important to have a column for comments as this can capture some data that we may not be aware of, such as a recent engine overhaul done to the unit, or the driver has regularly lost power over the past few weeks, or the driver tops up the oil every time he gets back to the yard.

These little details may not be captured in the CMMS (if one exists) or the maintenance logs, but they are crucial in determining whether we can safely extend the oil drain intervals or not. For units that require special attention or are under warranty, these may have to be excluded until more favorable conditions exist.

Based on the fleet (15 trucks), they were categorized into three main groups:

Critical – these units were being used every day on projects that had tight deadlines. They were often unavailable to return to the yard for maintenance or oil changes, as each hour away from the job affected the project deadline.

Semi-critical – these units were utilized by various customers at distant locations and often spent most of their time at the customer site (due to the distance). Hence, basic maintenance was usually performed at the customer’s site, causing minimal disruption to the operation.

Non-Critical – these units are often deployed in situations where extra assistance is required, or they are the standby units if one of the critical units is in trouble. 

Even though they had these three groupings, the engine types and mileages were very varied. Hence, a matrix was formed for this fleet.

The majority of the fleet falls within the 20-100,000km range, spanning across the critical, semi-critical, and non-critical categories.

A pilot test was done on the following:

• 3 of the critical units within the 20-100,000km range
• 1 semi-critical unit in the >100,000km range
• 1 non-critical unit in the > 100,000km range

Since the typical oil drain interval was 3,000km, we took samples at 1500, 2000, 2500, and then again at 3000km. Based on the trend observed from the first 3 samples, we had a fair indication of the condition of the oil before it got to 3000km.

None of the samples showed any unusual signs of wear, excessive additive depletion, or ingress of contaminants. For these samples, we kept a close eye on maintaining the following parameters:

Samples were then taken at 3500, 4000, 4500, 5000, 5500 & 6000km. Then, another set of samples was taken at 6500, 7000, 7500, and 8000km once the oil analysis values were still within range. The aim was to at least double the oil drain interval for this fleet.

Intervals of 500km were used as a cautionary value to allow enough time for any anomalies to be caught. The critical engines got to these values faster than the semi-critical and non-critical units.

All of the critical units easily got to 9000km without any of the oil analysis values entering the warning zones. However, the semi-critical unit, which had exceeded 100,000km, only made it to 8,500 km before the TBN and fuel dilution values entered the warning zone. The non-critical unit, which exceeded 100,000km, also reached 9,000 km without any issues.

Since the owner wanted to be on the side of caution (and allow some wiggle room between the intervals for trucks which could not get maintenance done at the specified interval), they chose to change the oils across the fleet at the 7500km mark but keep the oil analysis program where they perform samples at 4000 & 7000km.

They will now work alongside oil analysis, and for some trucks, where they believe they can have an even longer interval, they will extend it accordingly. 

What does this mean?

These engines take approximately 44 quarts or roughly 42 liters of oil and are changed every 3000km or roughly 2 months (critical units) with an average of 3 hours downtime for the oil change.

Hence, one unit undergoes approximately six oil changes per year:

• An average of about 3 hours x 6 times = 18 hours downtime
• An average of 42 liters x 6 times = 252 liters changed per year
• Thus, for six critical units that would be:
• Downtime => 18 hours x 6 units = 108 hours
• Oil consumption = 252 liters x 6 units = 1,512 liters

The new oil drain interval of 7500km resulted in a 2.5-fold increase in the interval.

This means that the new interval would be every 5 months instead of every 2 months.

Thus, these six units would only do oil changes twice for the year.

New downtime = 3 hours x 2 times/year x 6 units = 36 hours / year

New oil consumption = 42 liters x 2 times/year x 6 units = 504 liters

The following table summarizes the changes.

This is just for part of the fleet, and a dollar value has not been assigned to these, but clearly, there are lots of benefits to extending the oil drain interval through guided oil analysis.

Sensors vs Traditional Oil Analysis

In this age of AI, it seems that everyone is moving towards sensors and online data. Oil analysis sensors aren’t far behind in this revolution. There are mid-infrared sensors that have been engineered to relate their findings to those of regular oil tests (developed by Spectrolytic). While sensors are the way of the future, the fundamental concept remains the same. What are we doing with the data, and what data are we trending?

Sensors deliver speed, but proven lab methods still set the benchmark for accuracy.

Traditional oil analysis labs trend data, albeit the frequency of the data points is not as high as that of an online sensor. Hence, subtle/instant changes may not be readily noticed or detected. The methods used in these labs have been tried and tested over the years (and approved by various standards committees) to reflect conditions that the oil is facing in the field.

On the other hand, in the sensor world, not many of them (with the few exceptions) correlate exactly to what is being seen in the field. Hence, some lab tests, especially for the specialty tests such as RULER, MPC, and TOST (mainly for turbines), still need to be done by the lab. This is a great opportunity for traditional labs and sensor companies to collaborate and provide customers with collated data.

Moving Towards Sustainable Maintenance

While this article explicitly discusses extending the oil drain intervals for your assets, it underscores the importance of working alongside maintenance and condition monitoring to achieve these results. There is no clear cookie-cutter routine to achieve this, as each fleet of assets will be different and require varying levels of complexity for analysis. One thing is clear, though: we need to move towards sustainable maintenance.

Performing maintenance in the traditional way of just waiting for the appointed interval may be costing us increased labor and parts. However, by working alongside maintenance and condition monitoring, we can get more value from our assets and even increase our ROIs. Sustainable maintenance is the way forward for most asset owners as we move into a new era of maintenance.  

In industrial maintenance and reliability engineering, few concepts are as intuitively powerful as the Pareto Principle—also known as the 80/20 rule. Originating from Vilfredo Pareto’s observations of wealth distribution, this principle has evolved into a universal heuristic for identifying and prioritizing key contributors to outcomes in various systems.

When applied to oil analysis and lubrication in industrial sites, the Pareto Principle becomes a strategic lens for uncovering the few root causes that contribute to the majority of lubrication-related failures, inefficiencies, or costs.

This article explores how the 80/20 rule can be harnessed to improve lubrication practices, interpret oil analysis data more effectively, and drive proactive maintenance programs that prevent catastrophic failures and optimize equipment uptime.

Understanding the Pareto Principle in Maintenance Context

The Pareto Principle suggests that 80% of the consequences come from 20% of causes. In maintenance, this often manifests as:

• 80% of downtime comes from 20% of the assets.
• 80% of failures arise from 20% of the failure modes.
• 80% of lubrication problems stem from 20% of systemic causes.

It’s not always a perfect 80/20 split—it might be 70/30, 90/10—but the essence remains: a minority of inputs drive most results.

The Relevance of Pareto in Lubrication and Oil Analysis

In lubrication management, problems such as excessive wear, contamination, oxidation, or varnish are typically not evenly distributed across all assets. Often, a handful of machines or poor practices are responsible for most lubrication issues.

By applying Pareto thinking to oil analysis reports and failure data, organizations can identify:

• Which machines cause the most oil-related problems.
• Which contaminants or degradation modes (e.g., water, particles, oxidation) are most responsible for wear.
• Which human errors or process gaps (e.g., over-greasing, delayed oil changes) create recurring lubrication issues.

Common Lubrication Problems on Industrial Sites

Before diving into Pareto-based solutions, let’s categorize common lubrication problems observed in industrial environments:

Contamination

• Particulate contamination (dust, wear particles)
• Water contamination (condensation, seal failures)
• Cross-contamination (mixing incompatible lubricants)

Degradation

• Oxidation due to thermal stress or long drain intervals
• Thermal degradation due to temperature
• Varnish formation in turbine and hydraulic systems
• Additive depletion in modern synthetic oils

Human and Procedural Errors

• Improper lubricant selection
• Over or underlubrication
• Lack of lubrication schedules or documentation
• Incorrect oil sampling methods, leading to misleading analysis

Equipment Design or Condition

• Poor seals or breathers
• Inaccessible lubrication points
• Aging machinery with high internal clearances or hotspots

These problems create a web of interlinked failure causes—but applying a Pareto analysis can clarify which issues deserve immediate, focused attention.

Applying Pareto Analysis to Oil Analysis Data

Oil analysis laboratories and CMMS (Computerized Maintenance Management Systems) often collect thousands of data points. The challenge is prioritizing corrective actions based on this overwhelming data. Here’s how the Pareto Principle helps.

Step 1: Aggregate the Data

Collect a year’s worth of oil analysis reports across all critical assets. For each sample, extract flagged issues, such as:

• Particle Count > ISO 18/16/13
• Water Content > 500 ppm
• TAN > 2.0
• Wear Metals above alarm thresholds

Step 2: Categorize Failures

Group issues into categories:

• Wear (Iron, Lead, Copper)
• Contamination (Water, Dirt)
• Degradation (Oxidation, Nitration)
• Additive Depletion (ZDDP, Ca/Mg levels)

Step 3: Quantify Occurrence

Tally how many times each issue occurred. You may find, for example:

• % of reports show particle contamination
• % shows elevated wear metals
• % shows high oxidation
• % shows water ingress

Step 4: Visualize with a Pareto Chart or Table

Create a bar chart from most frequent to least frequent issue, with a cumulative percentage line.

This visualization immediately shows you that 80% of your oil analysis alerts may stem from just 2 or 3 primary causes.

Step 5: Target the 20%

Now focus efforts on reducing these top causes by investigating:

• Source of contamination (Are breathers effective? Is filtration adequate?)
• Lubrication schedules (Are intervals too long?)
• Training gaps (Do technicians know how to sample or top off correctly?)

Real-World Example – Pareto in Action

Steel Manufacturing Plant, Case Study

At a steel plant running 250 hydraulic systems, the maintenance team struggled with chronic equipment downtime linked to hydraulic fluid failures. They implemented a two-year oil analysis program and categorized around 1000 samples, 2 samples per asset per year.

One of the first problems the site must address is the report from the lubricant analysis service provider. The report does not identify the type of failure the oil is suffering from; simply, like most providers, they flag a value that is above normal or outside the limits and mention said value in the report.

Thus, all the results from the nearly 1,000 samples had to be uploaded to an expert system we designed together with the support of state-of-the-art data analysis tools. After data processing and correct application of the fundamentals of oil analysis, the results were as follows:

• 38% flagged for particulate contamination
• 24% for water ingress
• 9% for oil oxidation
• 7% for oil thermal degradation
• 8% for viscosity improver modifier degradation
• 14% excessive wear

Many of the samples shared at least two of the identified conditions, in some cases due to the creation of a vicious cycle between them. For example, the presence of water generates wear, and wear can generate more wear.

Applying the Pareto rule, the three main factors that affect the availability of the plant’s hydraulic systems are particulate contamination, water ingress, and excessive wear.

If this analysis were to stop here, and this is what happens in 90% of cases due to a lack of application of knowledge of the fundamentals of lubrication, the first thing that would be done would be to work on improving the filtration, handling, and storage methods of the plant, which is not bad and would have an immediate impact on the degree of cleanliness of the oils.

At the same time, water ingress, in this case and in many systems, is due to inadequate vent points and seals, which leads to water ingress into the system and consequently to the lubricant.

Let’s take a closer look at particulate contamination for a moment. Let’s look at this from several perspectives:

• Lubricant oxidation
• VI improver additive loss
• Thermal degradation

In all three cases, the result is an increase in the concentration of soft solid particles present in the oil.

And yes, the answer, dear reader, is in that word that has gone unnoticed before your eyes, soft, which is not hard, is not abrasive. The method used for particle counting must be appropriate, eliminating organic compounds and water. Once this first problem has been overcome and the count verified, it is necessary to determine what type of particles are present in the oil.

Thus, having corrected the particle counting method, changed oil analysis providers, and analyzed a set of samples under a microscope, it is evident that solid but soft particles are present in higher concentrations than abrasive ones. These soft particles do not have an abrasive effect on components and do not cause wear. They do reduce heat transfer, increase oil temperature, and induce premature oxidation.

Actions Taken:

• The 4 types of hydraulic oils used in the plant are replaced by others with better IV characteristics and a slightly higher working temperature range.
• Added breathers to some units
• Trained technicians in clean oil handling

Results After 6 Months and 220 Samples

The results in just six months show that 69% of the samples are under perfect conditions, compared to the previous situation where almost every sample has been flagged for a reason.

The Pareto principle is a tool used to identify those situations that cause the most problems. However, its applicability must be based on the context in which it is used; in this case, a simple analysis of the fundamentals of lubrication and oil analysis.

For over 35 years, I have been at the heart of reliability, lubrication, and operational excellence. In my experience, siloed decision-making between maintenance, HSE, and procurement often results in inefficiencies, increased risks, and missed opportunities for genuine return on investment (ROI).

While reliability engineers ensure equipment runs smoothly, procurement focuses on cost savings, and HSE manages compliance and safety, true success comes when these teams work together.  What if we could change this paradigm?

Siloed decisions waste ROI – true success demands cross-functional collaboration.

In today’s competitive environment, operational excellence is essential for success. EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) is used to evaluate a company’s financial health. It is also often linked to economic health and directly impacts maintenance, safety (HSE compliance), and procurement efficiency.

Companies use EBITDA internally to assess management performance and profitability without the effects of financing and tax strategies.

This article launches a three-part series that highlights how EBITDA principles can drive reliable maintenance strategies, foster collaboration between HSE and procurement, and deliver cost savings. We will also showcase how our specialized algorithms and templates ease this collaboration, bringing operational success closer than ever.

Let’s Understand EBITDA in the Context of Reliability Maintenance

EBITDA is not just a financial metric—it is a game-changer. It gives investors and business leaders a clear picture of a company’s true profitability by excluding non-operational costs. Unlike net profit, which factors in interest and taxes, EBITDA cuts to the core of an operation’s earnings potential, providing a clear view of the company’s operational profitability.

EBITDA is not just a financial metric—it’s a game-changer.

EBITDA is the key to unlocking long-term success in maintenance and reliability. It demonstrates how effectively a company manages operational expenditures (OPEX) while striking a balance with capital expenditures (CAPEX). Maintenance teams can reduce unnecessary costs, extend asset lifespans, and optimize labor efficiency by improving reliability and maximizing asset use. All these elements work together to boost EBITDA, driving profitability and sustainability.

How EBITDA Differs from Traditional Financial Metrics

Traditional KPIs like MTBF, OEE, and downtime costs track operational performance, but they do not fully capture the financial impact of maintenance decisions.

Integrating EBITDA into maintenance strategies enables companies to shift from a cost-focused approach to a value-driven strategy that directly impacts the bottom line. When maintenance aligns with EBITDA, companies can clearly show how investments in reliability enhance profitability, asset longevity, and efficiency.

Turning Reliability into a Financial Advantage

Reliability improvements lead to:

• Reduced unplanned downtime, ensuring higher asset utilization and revenue generation.
• Lower repair and replacement costs, minimizing unexpected expenditures.
• Extended equipment life, increasing return-on-asset (ROA) while decreasing capital expenditures (CAPEX) required for new asset purchases.
• Improved energy efficiency, reducing operational expenses (OPEX), e.g., fuel costs.
• Reduction in Scope 1 GHG emissions, thus aiding in meeting corporate ESG and Decarbonization goals

These benefits translate directly into higher EBITDA, making it easier to justify long-term investments in predictive maintenance technologies and reliability-centered maintenance (RCM) programs.

Justifying Investment in Predictive Maintenance Technologies

Predictive maintenance tools—such as AI-driven analytics, condition-based monitoring sensors, automated exception-based fluid sampling, and machine learning algorithms—help detect potential failures before they cause costly disruptions. While the initial investment in such technologies may seem high, an EBITDA-focused approach demonstrates the measurable ROI through:

• Fewer unplanned failures – Reduced emergency maintenance costs.
• Optimal spare parts inventory – Eliminating excessive stockpiling and procurement waste.
• Improved labor efficiency – Technicians focus on high-priority maintenance rather than firefighting breakdowns.
• Improved safety and TRIF metrics – Elimination of Live-Work by removing personnel from harm’s way.
• Increased asset utilization – Increased production and revenue generation.

For example, real-time oil condition monitoring (OCM) can detect early signs of wear, contamination, or fuel dilution, allowing companies to take preventive action before catastrophic failures occur. This reduces the total maintenance cost while improving EBITDA performance.

The Case for Reliability-Centered Maintenance (RCM)

RCM is a structured approach that ensures maintenance resources are allocated where they provide the highest value. By aligning RCM strategies with EBITDA objectives, organizations can:

• Prioritize critical assets that have the most significant financial impact.
• Eliminate unnecessary maintenance tasks that don’t contribute to reliability or safety.
• Implement a risk-based approach that balances cost, safety, and performance.

For instance, in industries such as oil and gas, manufacturing, mining, and power generation, failures in critical rotating equipment can result in millions of dollars in losses per hour. An EBITDA-driven RCM approach ensures that maintenance dollars are spent strategically, preventing losses while improving operational resilience.

Bridging the Gap Between Finance and Maintenance

One of the biggest hurdles in securing funding for maintenance programs is the disconnect between engineering and financial decision-makers. By framing maintenance investment management in terms of EBITDA impact and reliability, teams can build a persuasive business case that resonates with executive management.

Key steps include:

• Quantifying the financial impact of reliability improvements through these templates (e.g., “Reducing downtime by 5% will increase annual revenue by $2M”).
• Presenting total cost of ownership (TCO) analysis, demonstrating the long-term savings of proactive maintenance.
• Aligning maintenance metrics with company KPI’s, ensuring that reliability efforts contribute to the company’s overall profitability goals, including profitability, safety, and production.

By embedding EBITDA templates into maintenance strategies, organizations can move beyond seeing maintenance as a cost center and instead recognize it as a profit enabler. Predictive maintenance technologies and RCM programs are just operational necessities—they are financial strategies that boost profitability, increase competitiveness, and drive sustainable business growth.

Addressing CAPEX Challenges and Bridging the Gap Between Teams

A major challenge in maintenance and reliability is striking a balance between capital expenditures (CAPEX) and operating expenditures (OPEX). Companies often struggle to secure funding for predictive maintenance because capital expenditure (CAPEX) decisions are driven by short-term financial concerns rather than long-term benefits.

1. The CAPEX vs. OPEX Dilemma

• OPEX covers recurring costs like labor, spare parts, and lubricants, impacting immediate financial performance.
• CAPEX involves investments in equipment, reliability programs, and technology upgrades, requiring long-term financial justification, often involving a grueling budget process.

Bridging this gap requires collaboration between maintenance, HSE, and procurement teams, aligning on how reliability investments drive EBITDA growth and long-term success.

2. Breaking Silos for Cost Efficiency

For too long, maintenance, procurement, and HSE teams have operated in isolation, each focused on their own objectives, KPIs, and version of success. This siloed approach is costing companies millions of dollars in lost revenue opportunities, which, for publicly traded companies, also means lost shareholder value.

• Inefficient spending on parts and equipment due to disconnected procurement strategies.
• Delayed maintenance because critical components are not sourced in time.
• Increased safety risks from poor planning, rushed repairs, and unanticipated failures.

It is time for a fundamental shift that aligns reliability, safety, and financial performance under a shared vision. When these teams collaborate and use EBITDA as a guiding metric, they can make more innovative investments, prevent unnecessary expenses, and, most importantly, keep people and operations safe.

EXAMPLE: The Vital Maintenance, procurement, and HSE teams have operated in isolation for too long, of Real-Time Fuel Dilution Monitoring in Operational Reliability

Fuel dilution is a hidden yet powerful threat to the operational reliability of internal combustion engine (ICE) powered equipment. When fuel contaminates lubricating oil, it breaks down the engine oil viscosity, increases wear, reduces combustion efficiency, and drives up maintenance costs. A faulty fuel injector resulting from carbon loading, a broken tip, etc., can quickly reduce a SAE 40W engine oil to a much thinner SAE 0W oil. Left unattended, operating the ICE engine with engine oil that has been compromised due to fuel dilution will most certainly reduce the engine’s expected useful life. The amount of damage that can occur during a typical 500-hour oil sampling interval could lead to a catastrophic engine failure.

When reliability, HSE, and procurement work together, companies win.

Why Fuel Dilution Monitoring is Essential for EBITDA Optimization

• Prevents Engine Failures – Early detection of fuel dilution helps avoid catastrophic breakdowns, saving on repair and replacement costs.
  Extends Equipment Life – Continuous monitoring ensures optimal lubrication, allowing equipment to run longer.
  Maximizes Fuel Efficiency – Contaminated oil hampers performance, leading to increased fuel consumption. Real-time monitoring keeps everything running at peak efficiency.
• Increases Utilization – Continuous monitoring of engine oil viscosity ensures greater equipment utilization, leading to increased production and revenue generation.

By implementing real-time fuel dilution monitoring, companies can lower control of their equipment’s health, reduce failures, reduce fuel waste, and lower maintenance costs while increasing production, ultimately driving EBITDA growth.

Aligning Reliability, HSE, and Procurement for Maximum Impact

In today’s competitive landscape, integrating EBITDA-driven strategies into maintenance and procurement is not just important – it is transformative.

• Proactive maintenance powers EBITDA by reducing unplanned downtime and maximizing resources.
• HSE compliance safeguards safety, mitigating financial and legal risks.
• Strong procurement collaboration fuels cost-effective, high-quality parts and lubrication solutions.

When reliability, HSE, and procurement work together, companies unlock the potential for a sustainable, efficient, and financially sound reliability program. This is not just about equipment longevity but also driving profitability and gaining a fierce competitive edge.

Traditional fluid analysis relies on pre-set flagging limits to evaluate the severity of a sample. These limits, refined over time through statistical analysis, provide a baseline for assessing whether test results indicate any maintenance needs.

If a sample falls within acceptable limits, it’s a green light to continue operation. If not, the accompanying fluid analysis report offers insights and actionable recommendations to help pinpoint issues.

 At POLARIS Laboratories®, we’ve accumulated decades of data from millions of tested samples, building a massive database that includes lubricant type, grade, machine hours, filter type, equipment and component details, and more.

Using 25 years of historical data, we’ve developed flagging limits based on customer submissions and Original Equipment Manufacturers (OEM) guidelines. These robust thresholds provide reliable guidance for equipment maintenance.

 Imagine a system that uses this wealth of data to forecast how equipment health will progress and change over time, moving customers from reactive to proactive maintenance.

 But what if we could take this a step further? Imagine a system that uses this wealth of data to evaluate the current state of equipment health and forecast how it will progress and change over time to provide predictive maintenance far in advance.

The system is programmed to learn and improve itself over time as it processes more information. This means moving fluid analysis customers from reactive to proactive maintenance, from diagnostic analysis to predictive analysis.

 As data science technologies advance POLARIS Laboratories® is at the forefront reshaping the future of fluid analysis using Artificial Intelligence (AI) and Machine Learning (ML). Our team has just launched a new, robust, and powerful analysis engine that calculates and identifies indicators to generate a readable technical statement, resulting in a streamlined approach to understanding and acting on the information.

 By leveraging our vast data sets, we can customize and fine-tune flagging limits and continually adapt as new data from equipment and lubricants becomes available. This evolving analysis provides even more precise recommendations, creating a dynamic feedback loop where the system “learns” from patterns in component, equipment, and lubricant data along with historical results.

This engine represents the advanced capabilities of fluid analysis, combining data analytics with technology to drive reliability and improve maintenance recommendations, and it’s here. Introducing: Aurora.

 Aurora is rolling out across our laboratory network, continuously learning in the background to refine its predictions.

Customers won’t see changes on their reports right away, but rest assured, the system is advancing in the background to enhance future recommendations.

 As we continue to develop, improve, and perfect this technology, we’re trailblazing the fluid analysis industry in an effort to provide the most accurate, reliable, and timely results for customers so they can save more of their equipment.