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.

Moving Beyond Detection to Deliver Reliability

For maintenance managers and reliability professionals, identifying a problem is only the beginning. The real objective is preventing it from happening again.

In demanding industries with processing and manufacturing equipment that operate under high loads, harsh environments, and tight production schedules, when a gearbox, hydraulic system, or bearing fails, the cost extends far beyond replacement parts. Uptime, safety, and production are on the line.

Oil analysis plays a critical role in early detection. But when advanced diagnostics are applied, it becomes a powerful tool for root cause investigation.

Routine Testing Identifies the Symptom

Standard oil analysis parameters like wear metals, contamination levels, viscosity, oxidation, and particle counts are essential for trending and early warning. They answer important questions like: Is wear increasing? Is contamination entering the system? Is the lubricant degrading prematurely?

However, for reliability engineers tasked with eliminating repeat failures, elevated iron levels or rising particle counts just aren’t enough. Those results identify the symptom – not the failure mechanism.

Understanding the mechanism is what drives corrective action to prevent future problems.

Filter Debris Analysis: Examining What the Filter Captures

In high-load applications common in production and manufacturing operations, significant wear debris is often captured by the filter before it can be detected in a standard oil sample.

Filter Debris Analysis retrieves and evaluates that trapped material, providing a clearer picture of active damage. Using Analytical Ferrography and Micropatch testing to examine particle size, morphology, and composition, analysts can determine whether the debris originates from rolling element fatigue, gear tooth spalling, severe sliding wear and even break-in conditions versus progressive failure.

For a maintenance manager, this level of detail supports informed, smarter decisions; to continue operating under monitoring, schedule a controlled shutdown, or take immediate action. It replaces guesswork with evidence.

Define the Wear Mode

Analytical Ferrography testing uses magnetic separation and microscopic examination to characterize wear particles in detail. This method distinguishes between wear modes and can answer these questions:

Are wear particles caused by rubbing wear during normal operation?
Is it cutting wear caused by abrasive contamination?
Is lubrication film failure causing severe sliding wear?
Is it fatigue wear caused by surface distress?
Are chemicals causing corrosive wear?

For facilities where contamination control and lubricant performance are critical, identifying the wear mode is often the turning point in a root cause investigation.

For example, severe sliding wear may indicate inadequate viscosity selection or excessive load. Cutting wear often traces back to contamination control deficiencies in breathers, seals, or overall maintenance practices. Fatigue wear may reveal misalignment or load distribution problems.

Without microscopic confirmation, these distinctions are difficult to make. With it, corrective actions become targeted and sustainable.

Microscopic Contaminant Identification: Finding the Entry Pathway

Contamination remains one of the leading root causes of equipment failure across all industries. Advanced microscopic analysis can identify:

• Silica from environmental dirt
• Process materials unique to processing and manufacturing facilities
• Fibers from filters or cleaning materials

Reliability managers can use this information to address ingress points, refine storage and handling practices, or adjust filtration strategies. Instead of repeatedly changing oil, facilities can eliminate the source.

Turning Data Into Reliability Strategy

For reliability engineers, the goal is not simply monitoring machine health – it is building a no-surprises culture around asset performance.

Advanced oil analysis techniques such as Filter Debris Analysis and Analytical Ferrography transform a condition monitoring program into a diagnostic partnership. They provide evidence-based insight that supports root cause analysis, justifies maintenance planning decisions, and reduces repeat failures.

In high-demand production environments, that distinction matters.

Oil carries the story of what is happening inside your equipment. When you look beyond the numbers and examine the evidence, you move from reacting to failures to preventing them. And that is where real reliability gains are made.

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/

Oil analysis is widely recognized as one of the most potent tools in precision lubrication and reliability engineering. Yet, despite decades of industry experience, many organizations unknowingly undermine their effectiveness before the first sample is even taken.

A Real-World Example of a Hidden Oil Analysis Failure

Recently, we received an enquiry for an oil analysis call-off contract from a major industrial organization for their critical rotating machinery. The scope initially appeared comprehensive – until we examined the details. A closer review of the Scope of Work revealed a fundamental issue that, unfortunately, is becoming increasingly common across several industrial facilities.

The client attached the lubricant specification sheet, extracted directly from the OEM manual, assuming these parameters specified the tests to be performed on used oil samples during routine condition monitoring. When questioned regarding the selected oil analysis test package, the client reiterated that the OEM required the lubricant to meet the specifications stated in the Operation and Maintenance manual and therefore assumed that all such tests must be applied to used oil samples.

Where Oil Analysis Programs Go Off Track

This increasingly common assumption, observed in several recent cases, exposes a deeper industry-wide problem: a lack of understanding of what used oil analysis is intended to achieve.

New oil specifications and used oil analysis serve entirely different purposes.

OEM lubricant specification sheets are designed for one primary objective: To define the quality and performance requirements of fresh oil at the time of purchase and commissioning.

These specifications typically include properties such as:

• Viscosity grade limits
• Viscosity Index
• Flash point
• Pour point
• Density
• Rust-preventing characteristics
• Foaming characteristics
• Demulsibility
• Oxidation and ageing tests
• FZG or load-carrying capacity

All of these tests are essential – but only for qualifying new oil before it enters the system.

Once the oil is in service, the objective changes completely.

Used oil analysis is not about confirming what the oil was when it was new. It concerns understanding what is happening inside the machine at present.

Used Oil Analysis Is a Condition Monitoring Tool, Not a Compliance Checklist

In operating equipment – especially gas turbines, steam turbines, compressors, and hydraulic control systems – used oil analysis must answer particular reliability questions:

• Is the oil in healthy condition or degrading faster than expected?
• Is contamination entering the lubrication system?
• Are wear mechanisms developing inside bearings or gears?
• Is varnish or insoluble material forming?
• Are control valves, journals, or servo systems at risk?

Tests such as Pour point, Rust Prevention, Viscosity Index, or FZG ratings provide little to no actionable insight once the oil is in service. Meanwhile, critical failure mechanisms often go undetected when the wrong test slate is applied.

This is how organizations end up with beautiful laboratory reports but poor machine reliability.

What Goes Wrong When Spec Sheets Drive Your Oil Analysis Program

When OEM new-oil specifications are incorrectly used as an in-service oil analysis program, several things happen:

1. Early failure indicators are missed
   Parameters that actually trend degradation—such as varnish potential, water contamination, Particle cleanliness, and additive depletion—are either overlooked or underemphasized.
2. Oil analysis becomes reactive instead of predictive and proactive
   Issues are detected only after alarms, trips, or component damage occur.
3. Lubrication decisions lose credibility
   Maintenance teams receive reports that do not translate into clear actions, leading to distrust in oil analysis as a reliability tool.
4. Critical machinery reliability is compromised
   Bearings, journals, and hydraulic components fail prematurely – not solely due to oil quality, but also due to poor visibility into oil condition.

The True Purpose of Oil Analysis

To simultaneously assess three conditions:

1. Oil condition – how well the lubricant is holding up in service
2. Contamination condition – what unwanted materials are entering the system
3. Machine condition – what the oil is revealing about internal wear and distress of the machine.

When properly designed, an oil analysis program serves as an early-warning system, detecting degradation and failure mechanisms well before alarms, trips, or component damage occur.

However, this only works if the right tests are selected for the right purpose.

Before You Design an Oil Analysis Program, Audit Your Lubrication Practices

A robust oil analysis program should never be built by copying tables from OEM manuals.

Global best practice dictates that a Lubricant Benchmarking and Assessment Audit must precede the design of any oil analysis program.

A structured lubrication audit enables organizations to systematically identify gaps across all critical elements of machinery lubrication management.

• Lubricant Selection and Purchase
• Assess staff competency, training needs, and lubrication awareness
• Evaluate contamination control practices, including ingress prevention and filtration
• Review lubricant storage, handling, and dispensing methods
• Examine oil sampling practices and the condition monitoring program
• Verify machine-specific lubrication requirements and target cleanliness levels

Addressing these areas holistically ensures that oil analysis objectives are properly aligned with overall reliability and asset performance goals.

Oil Analysis Is Not a Lab Activity – It Is a Reliability Discipline

Oil analysis does not fail because laboratories lack capability. It fails because programs are often designed without a clear understanding of what needs to be detected, why it matters, and when it must be detected early.

The difference between oil analysis that merely reports numbers and oil analysis that prevents failures lies in program design, not testing volume.

More data doesn’t mean better reliability. Better questions do.

Oil analysis is one of the most powerful reliability tools available – when applied correctly. However, when new oil specifications are mistaken for used-oil condition monitoring, the entire purpose is undermined.

The industry does not suffer from a lack of data. It suffers from misaligned data.

Understanding the distinction between oil quality and oil condition is not a laboratory issue – it is a responsibility of reliability leadership.

Until that distinction is clearly understood, companies will continue to spend on oil analysis while still incurring unplanned downtime costs.

The reliability and availability of industrial assets are critical factors for the competitiveness of modern organizations. In this context, maintenance has evolved from a predominantly corrective model to preventive, predictive, and more recently, proactive approaches. Oil analysis stands out as one of the most effective tools for early failure detection, contamination control, and assessment of equipment operating conditions.

This article aims to discuss in depth the role of oil analysis as a strategic instrument in industrial asset management, grounding its application in the principles established by ISO 55001 and ICML 55.1. It is demonstrated that integrating oil analysis into a structured lubrication and asset management program contributes significantly to risk reduction, extending equipment service life, optimizing maintenance costs, and strengthening operational reliability.

Transforming Data into Reliability: Oil Analysis in Advanced Industrial Asset Management

The increasing complexity of industrial systems, combined with the demand for greater availability, safety, and operational efficiency, has driven the adoption of advanced maintenance and physical asset management practices. Failures in critical equipment result not only in production losses but also in significant impacts on personnel safety, the environment, and the corporate image of organizations.

Breakdowns are events; degradation is a process.

Although many failures are perceived as unexpected events, several studies indicate that most failure modes present detectable early warning signs over time (Mobley, 2002; Bloch & Geitner, 2019). In this scenario, condition monitoring techniques play a fundamental role in the early identification of progressive degradation processes.

Oil analysis, traditionally associated with evaluating lubricant condition, has evolved into a diagnostic tool capable of providing detailed information on the condition of internal equipment components, operating conditions, and the maintenance practices adopted. When properly applied, it becomes a central element of proactive maintenance.

The ISO 55001 standard, which addresses asset management, emphasizes the need for decision-making based on reliable data and aligned with organizational objectives. Complementarily, the ICML 55.1 standard establishes guidelines for developing robust lubrication programs, recognizing oil analysis as one of its essential technical pillars.

Asset Management and Maintenance: A Standards-Based Approach

ISO 55001 Principles Applied to Maintenance

ISO 55001 defines asset management as the coordinated activity of an organization to realize value from its assets throughout their entire life cycle. Among its fundamental principles are:

• Risk-based approach, considering failure probability and consequences;
• Evidence-based decision-making supported by reliable data;
• Strategic alignment integrating maintenance, operations, and organizational objectives;
• Continuous improvement through performance monitoring and organizational learning.

Within this context, maintenance shifts from a reactive function to a strategic role, using analytical tools to anticipate failures and mitigate risks. Oil analysis directly supports these principles by providing objective data on the actual condition of assets.

The Role of Lubrication According to ICML 55.1

The ICML 55.1 standard establishes the requirements for the development of world-class lubrication programs, structured around several pillars, including:

• Proper lubricant selection;
• Appropriate storage, handling, and application methods;
• Rigorous contamination control;
• Monitoring of lubricant and equipment condition;
• Technical training and competency development of teams.

Oil analysis is presented as an essential tool for validating the effectiveness of these pillars, providing objective indicators of the lubricated system’s health and enabling continuous adjustments to the lubrication program.

Technical Fundamentals of Oil Analysis

Oil analysis involves applying laboratory and interpretive techniques to evaluate the physicochemical properties of lubricants and detect contaminants and wear particles. Its value lies in the ability to correlate this information with the degradation mechanisms of internal equipment components.

Wear Metal Analysis

The identification and quantification of metals present in the oil allow inference of which components are undergoing wear and to what extent. Techniques such as optical emission spectrometry and ICP (Inductively Coupled Plasma) enable the detection of elements such as iron, copper, aluminum, chromium, and tin, each associated with specific components. The temporal evolution of these concentrations is essential to differentiate normal operating conditions from abnormal wear processes.

Analytical Ferrography

Analytical ferrography enables evaluation of wear-particle morphology, providing qualitative information on active wear mechanisms, such as abrasive, adhesive, fatigue, or corrosive wear. This technique is particularly relevant in failure investigations and root cause analysis.

Evaluation of Lubricant Properties

The analysis of the lubricant’s physicochemical properties—such as viscosity, total acid number (TAN), total base number (TBN), oxidation, and additive condition—allows assessment of whether the lubricant maintains its ability to form an adequate lubricating film under operating conditions. Degradation of these properties directly compromises component protection and accelerates wear mechanisms.

Oil Analysis in Failure Cause Identification

Identification of Affected Components

The correlation between detected metals, their morphology, and their rate of evolution enables identification of affected components and the severity of damage. This approach significantly reduces the time and uncertainty associated with failure investigations.

Evaluation of Lubricant Suitability

The use of an unsuitable lubricant, in terms of viscosity, additive package, or material compatibility, can result in premature failure. Oil analysis enables verification that lubricant properties align with equipment specifications and actual operating conditions.

Oil Analysis in Failure Cause Identification

Identification of Affected Components

The correlation between detected metals, their morphology, and their rate of evolution enables identification of affected components and the severity of damage. This approach significantly reduces the time and uncertainty associated with failure investigations.

Evaluation of Lubricant Suitability

The use of an unsuitable lubricant, in terms of viscosity, additive package, or material compatibility, can result in premature failure. Oil analysis enables verification that lubricant properties align with equipment specifications and actual operating conditions.

Diagnosis of Installation and Assembly Failures

Particles characteristic of fatigue or localized wear may indicate misalignment, imbalance, or improper installation of bearings, gears, and other critical components. These deviations, often undetectable through visual inspections, can be identified early through oil analysis.

Evaluation of Operating Conditions

The presence of large metallic particles or high particle concentrations may indicate overload, excessive speeds, or operation outside design limits. In this way, oil analysis serves as an indirect indicator of the actual operating conditions of the equipment.

Contamination Control

Contamination by water, solid particles, and foreign fluids is recognized as a primary cause of lubricant degradation and premature failure. Oil analysis enables identification of both the presence and the source of contamination, allowing corrective actions aligned with ICML 55.1 best practices.

Oil Analysis as a Pillar of Proactive Maintenance

Proactive maintenance seeks to eliminate the root causes of failures before they manifest in functional failures. In this context, oil analysis plays a central role by providing reliable data for:

• Failure anticipation;
• Condition-based intervention planning;
• Optimization of maintenance intervals;
• Reduction of unplanned downtime;
• Increased asset reliability and service life.

When integrated into an asset management system compliant with ISO 55001, oil analysis ceases to be an isolated activity and becomes part of a structured decision-making process aligned with organizational strategy.

The adoption of oil analysis as a strategic tool requires more than laboratory testing alone. Proper sample collection, qualified technical interpretation, trend history management, and well-defined corrective actions are essential. Organizations that treat oil analysis merely as a reactive practice fail to capture its full potential.

Conversely, when embedded in a structured lubrication program aligned with ISO 55001 and ICML 55.1 standards, oil analysis becomes a competitive advantage, enabling safer and more sustainable decision-making.

Oil analysis is an indispensable tool for modern maintenance and industrial asset management. Its structured application enables early failure detection, effective contamination control, and continuous improvement of operational reliability. Aligned with the principles of ISO 55001 and the practices recommended by ICML 55.1, oil analysis evolves from a monitoring technique into a strategic instrument for maximizing asset value throughout its life cycle.

References

• ISO 55001:2014 – Asset Management — Management Systems Requirements.
• ICML 55.1 – Lubrication Program Development Standard.
• MOBLEY, R. K. An Introduction to Predictive Maintenance. Elsevier, 2002.
• BLOCH, H. P.; GEITNER, F. K. Machinery Failure Analysis and Troubleshooting. Gulf Publishing, 2019.

In industrial and manufacturing facilities, lubrication and condition-monitoring programs focus on equipment that runs every day, such as compressors, gearboxes, pumps, turbines, and hydraulic systems that keep production moving. These critical assets are often routinely sampled, trended, and reviewed because their failure has an immediate and visible impact on operations.

But there is another side to operations that often receives less attention until it’s urgently needed in emergencies: backup power generators.

When backup power fails, the consequences aren’t minor – they’re catastrophic. You can’t troubleshoot an oil problem during a grid failure.

Backup generators are expected to perform flawlessly under the worst possible conditions, during storms, grid failures, or emergency shutdowns. Yet many facilities treat them as “standby” assets rather than mission-critical ones. The reality is simple: when backup power fails, the consequences can be severe, resulting in lost production, safety risks, equipment damage, and costly downtime.

Fluid analysis plays a critical role in ensuring backup generators are ready on a moment’s notice in the event they’re needed to maintain uninterrupted operations.

Standby Equipment, High Consequences

Unlike continuously operating industrial equipment, backup generators may run infrequently or under variable conditions. Long idle periods, short test runs, and sudden load demands introduce unique risks that traditional time-based maintenance alone cannot fully address.

Oil degradation, fuel contamination, and coolant issues often develop quietly while generators sit idle. Without routine fluid analysis, these problems remain hidden until startup, and when it’s far too late to correct them.

The worst time to discover a fluid problem is the moment you need your generator to perform.

Treating backup generators with the same condition-based mindset applied to primary assets is essential for reliability.

Oil Analysis: More Than Just Hours on the Meter

Oil analysis is often associated with runtime hours, but for backup generators, time and environment can be just as damaging as operation.

Routine oil testing helps identify:

• Oxidation and oil degradation during extended idle periods through Viscosity, Oxidation, and Base Number (BN) testing
• Contamination from dirt, moisture, or coolant leaks using Elemental Analysis by ICP and Water Content
• Abnormal wear metals indicating internal component issues through Elemental Analysis by ICP and Ferrous Debris Monitoring testing
• Improper viscosity or additive depletion that can reduce engine protection and lead to accelerated wear

Trending oil results over time allows maintenance teams to distinguish between normal aging and developing mechanical problems. This is especially important for generators that may appear “healthy” based on limited run hours but are slowly deteriorating internally.

The Most Overlooked Risk: Diesel Fuel

Fuel quality is a leading cause of backup generator failure. Diesel fuel can degrade significantly during storage, especially when exposed to moisture, temperature fluctuations, or poor housekeeping practices.

Routine diesel fuel testing helps monitor:

• Water contamination and condensation through Karl Fischer testing
• Microbial growth that leads to sludge and filter plugging through Microbial and Adenosine Triphosphate (ATP) testing
• Fuel stability through Thermal Stability and fuel acidity through Copper Corrosion testing
• Contamination through Viscosity and Flashpoint testing
• Identify fuel type and detect the presence of petroleum-based contaminants using Distillation, API Gravity, and Cetane Number testing
• Particulate contamination that can damage injectors using Particle Count testing and Elemental Analysis by ICP testing to detect signs of dirt and tank corrosion

Fuel-related issues often surface only during startup or load testing: exactly when reliability matters most. A proactive diesel fuel testing program enables corrective actions, such as fuel polishing, tank cleaning, or additive treatment, before an emergency occurs.

Coolant Analysis: Protecting the Engine You Can’t Afford to Lose

Coolant condition is critical to engine health, yet it is frequently under-tested in backup power systems. Depleted inhibitors, improper chemistry, or contamination can lead to corrosion, liner pitting, overheating, and premature engine failure.

Coolant analysis provides insight into:

• Freeze point and boil protection through Glycol Concentration and Freeze/Boil point testing
• Signs of glycol breakdown and degradation through Ion Chromatography
• Additive depletion and corrosion inhibitor health using pH, Reserve Alkalinity, Nitrite/Molybdate testing, and Organic Acid Monitoring through High Performance Liquid Chromatography (HPLC) testing
• Contamination from oil, diesel fuel, or external sources through Visual Analysis and Elemental Analysis by ICP
• Scale-forming minerals, improper coolant mixtures, and water quality indicators through Hardness, Chlorides, Sulfates, and Conductivity testing

Regular coolant testing ensures the cooling system can handle sudden load demands and temperature spikes when generators are required to run continuously.

Turning Fluid Data into Reliability

The value of fluid analysis lies not just in testing, but in using results to make smarter maintenance decisions. Trending oil, fuel, and coolant data together provides a comprehensive view of generator health and allows teams to prioritize actions based on condition rather than assumptions.

If you’re already trending fluid data on your primary assets, there’s no reason your backup generators should be flying blind.

For industrial facilities that already have strong lubrication programs in place, extending fluid analysis to backup generators is a natural and necessary step in maintenance planning.

Reliability Isn’t Optional

Backup generators may not run every day, but when they do, failure is not an option. Fluid analysis provides the earliest warning of developing issues and ensures these critical assets are ready when the unexpected occurs.

If your lubrication program protects the equipment that drives production, it should also protect the equipment that keeps everything running when production and safety are on the line.

It is very likely that you have encountered the RPVOT (Rotating Pressure Vessel Oxidation Test), a laboratory test developed in the 1960s and later standardized by ASTM D2272. It measures the antioxidant stability of lubricating oils by accelerating oxidation in a rotary pressure vessel.

The most common result is an integer expressed in minutes that measures the time until the pressure drops by 25 psi from the initial maximum (T0 to T1) under conditions of 150°C, oxygen at ~90 psi, and a copper catalyst. Some labs include the graphic that most closely resembles one of the most famous drawings in Antoine de Saint-Exupéry’s book, An Elephant Inside a Boa Constrictor.

Although this laboratory test has a lot to say about the state and condition of the oil, it has been assumed that the result depends only on the numerical value, being that it is much more than a simple number, and knowing this test in slightly more detail can be very useful when planning the lubricant. In this article, we will analyze the oil’s behavior during this test from an energy perspective. We will examine the relationship with other laboratory tests and, finally, the application of new methodologies to assess the test’s performance.

Let’s look at three samples of the same type of oil, a zinc-free mineral oil designed for gas and steam turbines with antioxidant additives, rust and corrosion inhibitors, and anti-foaming agents. According to the technical sheet, the RPVOT test of the new oil reports 1000 minutes.

Remember that the objective of the test is to force the oxidation of the oil.

RPVOT Regions

PART 1: Graph Analysis

Oxidation Induction Region, Gas Heating and Expansion

An initial period where antioxidants (phenolics, amines, phosphorus) neutralize free radicals (ROO-, O2-) formed by temperature (150°C) and Cu, H2O, and O2 catalysts. In this phase, the gas’s thermal expansion predominates, water evaporates, but oxidation is still low.

• Graphically: Low slope (<0.05 psi/min), stable pressure ~192 psi.​
• Entropy: minimum ΔS (+10-19 J/mol· K); under molecular disorder.
• Gases: Very few volatiles (residual H2O).
• Duration: ~35-100 min; longer in sample 2 (additives in good condition).

Region of Propagation of Quasi-Stationary Oxidation

The primary antioxidants are exhausted; the oxidative chain begins when free radicals attack the hydrocarbons of the base oil, initiating a sequence of peroxide formation, aldehyde formation, and acid formation, leading to varnish (degradation byproducts). In this phase, O2 is rapidly consumed.

• Graphically: Tipping point, average slope (-0.05 to -0.2 psi/min), drop ~192→175 psi.
• Entropy: High ΔS (+25-32 J/mol· K); Disorder increases due to the increase in molecular fragments of the hydrocarbon chain.
• Gases: CO, CO2, volatile hydrocarbons cause ~10-20 psi drop.​
• Sample 3 has a steeper curve due to the consumption or condition of additives.

 Region of Termination or Pressure Drop

The secondary inhibitors present in this oil, such as phosphates, recombine the remaining radicals, and in this phase, the oxidation slows, but the presence of residues is evident, which can increase viscosity and AN.

• Graphically: Low slope (<-0.2 psi/min), stabilization ~170 psi.
• Entropy: moderate ΔS (+16-22 J/mol· K); residual disorder.
• Gases: Minimal, but there is evidence of varnish or accumulated sediment.
• Sample 2 is more stable; this is evidenced by a flat curve.

Analysis by Sample

Sample 2 (more stable, total ΔP ~22 psi in ~300 min): Low degradation, additives are efficient and limit clutter.​

• Induction Region (t≈48-100 min, 192.1→190.5 psi): ΔS = +10.2 J/mol· K and phenolic antioxidants intact.
• Propagation Region (100-220 min, 190.5→182 psi): ΔS = +24.8 J/mol·K. With a moderate concentration of peroxide.
• Termination Region (>220 min, 182→170 psi): ΔS = +16.5 J/mol·K. Total: +51.5 J/mol·K.

Sample 1 (mean degradation, ΔP ~23 psi): a higher initial additive consumption.​

• Induction Region (46-90 min, 192→183 psi): ΔS = +14.7 J/mol·K.
• Propagation Region (90-230 min, 183→173 psi): ΔS = +27.1 J/mol·K.
• Termination Region (>230 min, 173→169 psi): ΔS = +19.2 J/mol·K. Total: +61.0 J/mol·K.

Sample 3 (the most degraded, ΔP ~25 psi): High early oxidation.​

• Induction Region (35-80 min, 192→167 psi): ΔS = +18.9 J/mol·K. The exhausted phosphate additive is exhausted.
• Propagation Region (80-260 min, 167→160 psi): ΔS = +32.4 J/mol·K. Increase in volatile compounds and acid production.
• Termination Region (>260 min, 160→155 psi): ΔS = +22.1 J/mol·K. Total: +73.4 J/mol·K.

PART 2: Relationship with Other Laboratory Tests

The relationship between RPVOT and other laboratory tests is very weak and unreliable, and it is unlikely to predict a low RPVOT result.

Following our case study, we have:

On the other hand, the repeatability and reproducibility of the RPVOT is low and this greatly reduces its reliability when making a decision in case of results that do not meet the expectations of the end user.

So, what laboratory analysis can be a good ally when performing the RPVOT? As Table 2 shows, traditional tests do not correlate with RPVOT results. However, there are two analyses that do show potential problems and can be used to work in conjunction with this test; Differential pulse voltammetry (DPV) is a powerful electroanalytical technique designed to measure the concentration of redox-active species with high sensitivity and resolution, often allowing the detection of analytes at concentrations as low as 10^-8 to 10^-9 molar. The next is FTIR, which allows identifying compounds in the fluid.

PART 3: Price Matters and Goes Hand In Hand with Innovation

Depending on the country and laboratory, the cost of an RPVOT analysis is usually between 150 and 650 $US, with an estimated turnaround time for results easily exceeding 10 days, not only because of the time it takes to test but also because samples may be on hold. If it is simply to comply with the oil manufacturer’s recommendations or the turbine OEM’s warranty, the end user will pay little attention to the result or the graph in the report.

The analytical tools available to us today have facilitated many aspects of our lives, including data analysis and the detection of patterns that help us identify future behaviors. Within the scope of RPVOT, in mid-2024, I was fortunate to develop an analytical tool that I called RPVOT_[SYN]. Its function basically aims to minimize two key aspects of this laboratory test: time and cost.

Thus, by applying a Bayesian analysis, it is possible to determine with a degree of confidence greater than 75% what the expected result of the RPVOT of a turbine oil will be. For a fraction of the cost of the laboratory test and in less than 6 hours, you get a result very similar to this:

RPVOT_[SYN] : 180 – 200

Some Recommendations: What Is Important When Receiving the RPVOT Results?

We said before that the numerical result matters, but it is not the only thing that should matter in this test. If you work with a respectable laboratory with sufficient technical knowledge, they will be able to report the graph (even in csv format) in such a way that it can be analyzed from the point of view of their regions.

Remember, the regions say much more than the simple numerical result. Cross-referencing this information with FTIR and DPV can yield much more insight into the condition of the lubricant. And it can help plan, in conjunction with other tests, a possible intervention on the lubricant, whether it’s a partial or complete change, or finding a solution due to problems related to the lubricant’s chemistry.

Don’t miss the opportunity to get the most out of an analysis of this caliber; it can tell you a lot about the condition of your lubricant.

Maintenance teams are entering a new era, one where data-driven insights enable automation and transform how equipment health is monitored and maintained. For years, the industry has been moving from preventive maintenance to predictive maintenance, but what’s next? It’s predictive reliability.

For lubrication and condition monitoring programs, this means harnessing connected data and smarter systems to make faster, more accurate decisions that predict problems before they impact uptime.

Setting the Foundation

To move toward actually predicting maintenance, the foundation and the data must be strong.

Condition monitoring relies on continuous streams of information from oil analysis, vibration sensors, thermography, and telematics. However, these data sets are often siloed, inconsistent, or manually reported, which significantly limits their usefulness. Data standardization is key. Standardized data formats, consistent naming conventions, and unified reporting structures enable automated systems and machine learning models to analyze, interpret, compare, and act on information.

Without reliable data, even the most advanced AI platform can’t identify meaningful trends or correlations. Prioritizing standardized data processes ensures that every data point can be confidently integrated into reliability systems.

For POLARIS Laboratories®, data standardization comes into play with customers’ database of equipment, OEM, components, assets, lubricant manufacturers, and those of the like. It’s imperative that this data be kept accurate, up to date, and as complete as possible.

Cloud-Based Reliability Systems

Cloud technology has accelerated this transformation by centralizing condition monitoring data. Through cloud-based platforms powered by API connections, maintenance and reliability managers can securely access results, trends, and recommendations in real time – from anywhere in the world.

For example, POLARIS Laboratories’ API integration, HORIZON® Connect, automatically feeds oil analysis results into a customer’s Computerized Maintenance Management System (CMMS) or Enterprise Resource Planning (ERP) system. This eliminates the need for manual data entry, reduces delays, and creates a single source of truth for decision-making. Cloud systems also support continuous learning; as more data is analyzed, AI-powered models can refine predictions and improve reliability.

AI and Condition Monitoring

Integrating artificial intelligence into condition monitoring practices can enhance what traditional analysis can achieve. Instead of reviewing static data points, AI algorithms evaluate trends over time and across equipment fleets, identifying subtle anomalies that might signal early signs of wear or contamination.

For example, AI can detect minor deviations in lubricant properties, such as gradual increases in oxidation or shifts in base number, that precede measurable wear. When integrated with other data streams, such as vibration and load, these patterns can pinpoint root causes of failure before they escalate. Rather than waiting for sensor alarms, maintenance teams can receive predictive insights that guide them to act sooner.

This is especially valuable in lubrication analysis, where AI models can compare millions of test results across different equipment types, environments, applications, and formulations. The result is a system that continuously learns and improves its ability to predict issues and recommend maintenance actions.

Feedback Loops and Continuous Improvement

AI and Machine Learning technologies thrive on feedback loops. In a condition monitoring program, every maintenance action (such as replacing a bearing or adjusting oil-drain intervals) generates data that feeds back into the condition monitoring system.

With an established API connection between oil analysis and maintenance systems, this also provides feedback to the laboratory, which can, in turn, improve analysis of future samples.

Setting up this feedback loop can help assess the effectiveness of those actions and adjust future predictions accordingly. Over time, the system becomes increasingly accurate and capable of recommending optimal interventions with minimal human oversight.

For lubrication management, this feedback cycle ensures that maintenance strategies evolve in sync with real-world equipment performance. The result is a more adaptive, efficient maintenance process that maximizes asset health and minimizes waste.

Clearing the Path Forward

As reliability teams prepare for the next generation of condition monitoring, the integration of AI, cloud-based platforms, and strict data standardization will define the leaders in uptime, safety, and operational efficiency. The future of reliability is intelligent, connected, and powered by data.

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.