I had a laboratory manager, over twenty years ago, complain to me that he wished I would stop pushing particle counting, as he was getting annoyed by clients asking for it after my courses.  In his view, particle counting was necessary primarily for hydraulic oils and gas turbine oils. He stressed that PQ readings (ferrous density analysis) were far better than particle counting.  Nothing that I could say would ever change his mind.

Admittedly, having been heavily involved in all forms and techniques of particle counting in the past, I may be biased. Still, particle counting plays a role in all forms of machinery for used oil analysis.

So, the question is: how can we ensure the two are used correctly and complement each other?

Predictive or Proactive?

The term Predictive Condition Monitoring is used a lot on LinkedIn to the extent that I begin to wonder whether people really understand that whilst predicting failure is a cost-benefit, preventing failure is essential to achieving reliability and sustainability in a safe and green plant.

A proactive approach considers the root cause of failure rather than simply predicting failure. Solid particles are among the leading causes of failure in most applications and industries. Indeed, this can vary across industries and machine types, where moisture is a significant root cause of contamination.

Understanding Wear Debris Generation

Before we can get into the issue of particle counting versus ferrous density testing, let’s revisit the issue of wear debris generation.

In normal rubbing wear, debris is generally small and low in number.

However, as the wear rate increases, the amount of material generated increases, but more importantly, so does the size of the debris.

Particle Counting for a Proactive Approach

Simply put, monitoring particle counts helps identify what’s happening.  It’s not quite that simple, however.  We need to determine whether the observed trends reflect increased particle ingestion or increased normal wear material.

In the early 1980s, when the ISO 4406 cleanliness code was developed, the idea was that particles larger than 5µm were considered silt, while particles larger than 15µm were considered chips.  Silt is generally considered dust particles ingested with normal wear debris.  Chips are typically the result of abnormal wear. 

Consequently, a rise in silt is likely due to either increased levels of ingested external solid particulate or increased normal wear, the latter indicating an issue.  Either way, both are an alert to a problem.

However, a rise in chips is definitely of concern as that indicates the onset of abnormal wear.

The Limitations of Particle Counting

The reason many commercial laboratories avoid particle counting for machinery other than hydraulics is simple.  Most rely on the light obscuration or light scattering particle counters for various reasons, namely:

1. Light blockage/light scattering units comply with ISO 11500:2022 – Hydraulic fluid power — Determination of the particulate contamination level of a liquid sample by automatic particle counting using the light-extinction principle” and thus tie in with other compliance requirements.
2. They require a small sample volume, typically less than 15mL.
3. Bench units are typically automated for high-volume use.

However, the downside is that anything other than relatively clean, dry oil will cause an issue, namely:

1. The flow through the sensor is restricted, so large debris could block the device and hold up the testing of samples as it is cleared.
2. Water droplets and air bubbles are counted as solids.
3. Dark fluids can be difficult for the light to penetrate.
4. Soot in engine oils, while technically not counted as per ISO 4406, owing to their tiny size, will cause issues with blinding the sensor.

While the above can be addressed by properly preparing a sample to eliminate air and water, this is time-consuming for a high-turnover laboratory.

Indeed, recent advances in light-blocking technology have reduced the occurrence of water droplets and air bubbles, but this still poses a problem with gear oils in particular, owing to the often-present issue of large debris, even if the water issue has been, or can be, addressed.  Note that many industrial gear oils have higher viscosity grades, which would hamper flow through the sensor.

It is easy to see why commercial laboratories would shy away from using gear oils for particle counting, and the same applies to high-soot-load engine oils and to water in the case of steam turbine and paper machine oils.

Hydraulics are perhaps the most sensitive to particle ingress, and, fortuitously, these are typically of lower viscosity grade, are clean and dry, and have significantly less wear debris, which is typically much smaller.

Ferrous Density Analysis Techniques for a Predictive Approach

As the name implies, ferrous density analysis measures the concentration of iron in the oil sample.  Unlike spectrographic oil analysis at the elemental or atomic level, it is not limited by debris size.  With elemental analysis utilising inductively coupled plasma or arc-spark technology, wear debris particles exceeding 8µm create a blinding spot. 

Simply put, the concentration of the atoms on the surface of the debris is detected, but the atoms of the inner body of the larger wear debris are not detected.  This has been addressed using X-ray Fluorescence Technology, which is more accurate in this context, but it is not widely used in many laboratories.

In ferrous density analysis, various approaches rely on magnetism or the Hall Effect to determine the result.  The three primary techniques are:

1. Particle Quantifier – The ANALEXpql was initially developed by the late Dr. Mervyn Jones of the Swansea Tribology Centre and is now a product in the Parker Hannifin range of instrumentation. Based on Hall Effect measurements, it reports a value on a 0–200 scale, with higher values indicating greater ferrous material in the sample. 
2. Direct Reading Ferrograph – The Trico DR-7 Direct Reading Ferrograph unit uses a magnetic gradient to trap ferrous debris and measure the amount of small (<5µm) and large (>5µm) debris to provide a Wear Particle Concentration, as well as a Percentage of Large Particles.
3. Coil measuring technology – The Spectro-Scientific FerroCheck 2000 utilises a pair of precision, rounded coils that, when powered, generate magnetic fields. When a small amount of in-service oil is introduced into one of the coils, ferrous particles interact with the magnetic field, inducing current changes in the coil. The amount of current change is proportional to the amount of Ferrous particles in the oil and can output a result in parts per million (ppm) as per ASTM D8120-17 Standard Test Method for Ferrous Debris Quantification
4. Another approach is that users of portable particle counters have been able to use a strong magnet to separate the Ferrous material from a sample following a particle count, and then repeat the count minus the Ferrous material to see how significant the percentage is. In addition, the trapped Ferrous material is analyzed using a microscope.
5. A simple magnetic sump plug or “mag-plug” is a fundamental form of Ferrous density trending; however, a photograph should be taken during inspection to assist with trending the amount of material trapped at each inspection.

In most commercial laboratories, my experience is that the PQ unit is more common outside North America, while the DR unit is more popular in North America.

For more details on the ANALEXpql and FerroCheck 2000, I suggest you also read the excellent article by Bryan Debshaw and David Swanson in Precision Lubrication, “Assessing Spectroscopic Methods to Analyze Particles: PQ vs. FerroQ” – Assessing Spectroscopic Methods to Analyze Particles: PQ vs. FerroQ.

The Limitations of Ferrous Density Testing

By the very nature of relying on magnetism to trap the Ferrous material or measure it via the Hall Effect, then clearly the focus is on Ferrous debris.  Moreover, the magnet is more likely to attract the larger material, too.

Consequently, while this approach works well on gearboxes that by their nature generate more wear debris, typically of a Ferrous nature, it is less successful on other machines where the amount generated is significantly less and more varied, with the focus needing to be on the non-Ferrous material.

Unlike the Direct Reading Ferrograph unit, the ANALEXpql cannot differentiate between small and large debris, either. However, there are ways around this by shaking the sample and observing how the PQ reading rate changes.  A slow increase in small debris, a rapid increase in larger debris.  The actual oil volume and the sample bottle size may also affect the result, so consistency is required to avoid erroneous results.

For most high-volume commercial laboratories, the ANALEXpql and FerroCheck 2000 are better suited in terms of test time and ease of sample preparation.

Particle Counting and Ferrous Density Testing?

Running both particle count and ferrous density tests together will ensure that the full range of particle sizes can be detected and that changes at any size are captured.  While elemental analysis is an indicator of the presence of smaller material, it reports only concentration, not the number of particles or their size.

While several laboratories will simply not accommodate that request for both techniques, the use of a portable particle counter, such as the Pall PCM500 unit that uses mesh blockage, as this, while not conforming to the ISO 11500, does not suffer from the problems of dark, high viscosity oils contaminated with larger wear debris or water. 

Similarly, these units are not affected by high soot loading on engine oils.  The Pall instrument is ideally suited to on-site use; however, most laboratories do not use this method because it is technically classified as a trending device rather than a counting device per ISO, and it requires a sample volume of over 200mL.

In the last decade, the Laser Net Fines unit has proven effective, offering both particle counting and a breakdown of wear debris by wear mechanism, while also differentiating water and air in the sample. It complies with ASTM D7596 – Standard Test Method for Automatic Particle Counting and Particle Shape Classification of Oils Using a Direct Imaging Integrated Tester.

In Summary

Whichever way you choose to approach this, remember that particle counting provides insight into what is happening within the machine based on the size and number of solids, and enables proactive intervention to prevent failure.  Wear debris is the result of a root cause issue, whether that be looseness, misalignment, imbalance, or a lubricant-related cause.

Ferrous density testing is a reliable indicator of increasing ferrous material levels. Sadly, for most end-users, and given how their oil analysis programmes are set up and managed, the data is often seen too late. The problem results in downtime and machine intervention rather than a proactive correction during machinery uptime.

Tan Delta Systems has announced the launch of its newest product, SENSE-2, an oil condition monitoring system aimed at the manufacturing and industrial sector. The company calls SENSE-2 “an innovative new system designed to optimize maintenance scheduling and dramatically reduce operating costs.”

The SENSE-2 system utilizes patented sensor technology and analytics to provide real-time monitoring of molecular changes and contaminants in lubricating oil. This data is intended to identify optimal timing for oil changes based on actual degradation instead of prescribed intervals.

Chris Greenwood, CEO of Tan Delta Systems, is quoted as saying, “SENSE-2 finally brings smart, condition-based maintenance to the manufacturing sector…By monitoring oil quality continuously, we can detect subtle equipment issues early and recommend maintenance at exactly the right time.”

For on-the-go oil analysis, the OQSx-G2 sensor can be purchased as part of the Mobile Oil Tester (MOT) kit, tailored for field maintenance teams. This portable configuration allows oil from industrial equipment to be easily sampled and tested in seconds directly at the site.

The kits provide sampling bottles that which oil can be collected in. By connecting the sensor to a Windows laptop, tablet, or PC installed with the MOT software app, the oil sample can be tested by following simple on-screen prompts. The streamlined process enables rapid on-location oil quality checks by maintenance personnel rather than relying solely on sending samples offsite.

Learn more about the Tan Delta SENSE-2 at tandeltasystems.com.

Condition monitoring sensors for lubricants have been an established technology for numerous decades, playing a vital role in safeguarding some of the most essential machinery worldwide, including their incorporation into certain premium automotive models.

However, it is somewhat paradoxical that lubricants within rotary machines, such as turbines and compressors—equipment fundamental to electricity generation and numerous chemical and industrial operations—rarely utilize remote sensing for condition monitoring.

This discrepancy can be attributed to two primary factors. First, the longevity of turbine oils, particularly within older steam or hydroelectric turbines, has deemed quarterly oil analysis sufficient for tracking condition trends.

Second, the performance of sensors available to these applications thus far has demonstrated inadequate correlation with analytical laboratory results.

But the landscape is evolving. Modern gas turbines operate at higher temperatures, imposing unprecedented thermal stress on their lubricants. Compressor systems present an even more demanding scenario for turbine oils, contending with thermal challenges, contamination from external gases, and, in some instances, exceedingly high operational speeds.

Concurrently, there is a burgeoning requirement for remote monitoring capabilities that minimize the necessity for physical, on-site maintenance. Against this backdrop, the imperative for real-time oil condition monitoring is intensifying.

This document examines a new real-time turbine oil monitoring development that employs infrared spectroscopy. It further elucidates the capacity of this technological advancement to meet the escalating demand for sophisticated sensors in this space.

Why Turbines and Turbine Oils Fail

Oil condition monitoring serves three primary technical objectives:

• Monitoring Lubricant Degradation Trends: This involves systematically tracking the degradation and aging process of the lubricant over time, which is crucial for maintaining optimal lubrication performance.
• Detecting Machinery Health Deterioration: The analysis aims to identify any signs of machinery wear or failure, which can be inferred from changes in the lubricant’s properties or composition.
• Quantifying Contaminant Levels: This includes the measurement of both external contaminants (such as dirt and water) and internally generated by-products (like wear metals and varnish). These contaminants can significantly impact the functioning and longevity of the machinery.

Given these purposes, it becomes clear that a thorough understanding of both turbine operation and the failure mechanisms of turbine oils is fundamental to effectively determining oil condition monitoring strategies.

Turbine Failure Modes

The most common modes of failure for turbines[1],[2],[3] are:

• Creep and Fatigue – which are caused by high temperatures and stress cycles. These can cause deformation, cracking, loss of efficiency, weakening of metal components, and rupture of the steam turbine components.
• Oxidation and corrosion are the chemical reactions of metal components with oxygen and other environmental substances. Oxidation and corrosion can cause pitting, scaling, and erosion of the turbine blades and other parts, reducing their strength and durability.
• Erosion is metal components’ physical wear and tear by abrasive particles or fluids. Erosion can cause material loss, roughness, and damage to the turbine blades and other parts, affecting their aerodynamics and performance.
• Steam-path distortion and/or blade/nozzle mechanical damage is caused by foreign-object damage, solid-particle erosion, cracking, and moisture erosion. These can affect a steam turbine’s flow, efficiency, and performance.
• Babbitt bearing failures, which are caused by babbitt fatigue, babbitt wiping, babbitt flow, foreign particle damage, varnish build-up, electrostatic discharge damage, electromagnetic discharge damage, oil burn, loss of bond, chemical attack, pivot wear, unloaded pad flutter, and cavitation damage. These can affect the alignment, stability, and vibration of the turbine.

Most turbine failure modes are not readily identifiable through oil sensor analysis, except for bearing health assessment. To effectively monitor the condition of turbine bearings, it is essential to employ sensors, including:

1. Temperature Probes: These devices measure the temperature of the bearing, which is a critical parameter. Elevated temperatures can indicate excessive friction, shear-stress degradation, misalignment, or lubrication issues, all precursors to bearing failure.
2. Vibration Sensors: These sensors detect and analyze vibrations emanating from the bearings. Variations in vibration patterns can signal issues such as imbalance, misalignment, bearing wear, or other mechanical faults.
3. Analysis of Varnish Potential in Oil: Assessing the potential for varnish formation in the oil is crucial. Varnish can be deposited on bearing surfaces and other critical components, leading to operational inefficiencies and potential failure.

Integrating these monitoring techniques can achieve a more comprehensive understanding of bearing health within turbines.

Turbine Oil Failure Modes

The primary cause of degradation in turbine oils is oxidation, although various other degradation mechanisms also play a role[4].

To assess the physical and chemical alterations occurring in turbine oil as a result of oxidative stress, the Turbine Oil Performance Prediction (TOPP) test is employed. This test measures how turbine oils change and deteriorate under oxidative conditions[5].

An example of a 12-week TOPP test can be viewed in Figure 1. Throughout the test, one can observe a rapid drop in phenolic antioxidants.

The amine antioxidants deplete similarly to the oxidative stability measured by RPVOT. Throughout the test, increasing MPC values show a high propensity for the oil to develop deposits. When selecting an appropriate sensor technology, considering turbine oil failure modes is essential.

An Oil-related, Catastrophic Failure Mode in Some Gas Turbines

A rare yet severe failure mode linked to turbine oil degradation has been observed in industrial gas turbines. This issue arises when the antioxidants within the turbine oil are exhausted, yet the oil continues to be used.

The situation becomes critical when an additional catalytic factor contributes to the chemical reaction. Under these conditions, the oil may undergo a process known as condensation polymerization, leading to the formation of long-chain cyclic compounds enriched with esters and acidic components.

The consequence of this chemical change in the oil is a dramatically accelerated degradation rate, which can be identified by increased deposits and an exponential rise in viscosity and acidity.

For the turbine, this scenario typically results in bearing failure and coating all oil-wetted components within the system with a highly adhesive and tenacious deposit. This deposit can significantly impair the turbine’s performance and necessitate extensive maintenance or repairs.

An example of trend analysis from this failure mode can be viewed in Figure 2.

Sensor Selection

The market offers diverse sensor technologies, each delivering distinct value across different applications. The selection of an appropriate sensor necessitates a thorough analysis of both the lubricant’s failure modes and the operational characteristics of the machine.

It’s important to note that sensor technology may be highly beneficial in one context yet offer minimal value in another.

Consider the following examples:

• Inline Viscosity Sensor: This sensor is particularly valuable in methane screw compressors, where gas ingression can cause rapid decreases in viscosity. In contrast, its utility is limited in turbine applications, where viscosity changes are uncommon. For instance, regarding the oil degradation previously discussed (Fig. 4), monitoring antioxidant health or detecting water ingression would be more predictive measures than tracking viscosity changes.
• Chip Detector: A chip detector can be an early warning indicator of impending engine failure in a fighter jet. However, its value is significantly reduced in steam or gas turbines. Wear metal formation is infrequent in these applications, and installing sensors directly downstream of each bearing for chip detection is logistically complex and often impractical.

Infrared Spectroscopy

The sensor platform developed by Spectrolytic utilizes the powerful analytical technique of mid-infrared spectroscopy to measure various relevant degradation parameters in an oil sample.

With each measurement, the sensor determines the changes in the oil at a molecular level using the same analytical technique and data extraction employed by oil laboratories worldwide.

The laboratories conduct an oil analysis on customer samples by applying various ASTM and/or DIN standards to determine Oxidation, Nitration, Sulphation, additive changes, and oil contamination levels.

This common baseline of using mid-infrared spectroscopy as the analytical tool not only allows the sensor to provide real-time data with the same units and accuracy as the oil laboratories, but it also provides the option to predict more complex oil parameters such as Total Acid Numbers (TAN), Total Base numbers (TBN) or ipH.

Another factor that should be considered is the simplicity of generating calibration files for any given oil/application. We have developed and designed the systems to allow the customer to monitor most parametric data from the day of installation.

There is no need to have old oil samples, ship oil samples around the world, or age the oils artificially.

Spectrolytic’s sensor platform has been installed in many different applications such as power generation (natural gas and biogas engines, gas turbines), aluminum and steel processing, and marine application (Marine Diesel and EAL oils for thrusters), showcasing the versatility and accuracy of the mid-infrared sensor platform.

Figures 3 and 4 compare lab analysis and inline sensor data for a natural gas engine application. There is an excellent agreement between predicted sensor data and reference analysis, as shown in the plots below.

The plot also displays ‘rogue’ measurements from reference analysis that the sensor does not display. This is likely some error in sample taking or sample measurement.

The sensor, therefore, removes all human involvement from the oil analysis process as there is no sample-taking process, no storage/shipment required, and no lab technician needed to carry out the measurements.

The results are reliable and consistent with the reference analysis, providing customers with accurate, understandable, and actionable oil analysis data 24/7.

Today’s oil formulations are complex and depend primarily on the application (engine, hydraulic, gearbox, etc.) regarding the base oil and additive packages used.

Base oils from Group I-III are mineral oils (even though some Group III base oils are classed in terminology as ‘synthetic’), and Group IV (PolyAlphaOelfins) and Group V (PolyAlkyGylcol, Polyolesters, Phosphate Ester, siloxanes, etc.) are classed as synthetic oils.

Multiple degradation mechanisms happen in parallel once a lubricant is used in an application. Turbine oil degradation is depicted below in Figure 8.

For any sensor technology to provide accurate, meaningful, and actionable data, the respective sensor technology must be able to DIFFERENTIATE between the respective degradation mechanisms. It must be able to QUANTIFY each degradation mechanism.

Spectrolytic took on this challenge with the development of its FluidinspectIR™ product line. It is now possible to offer customers real-time oil analysis with lab-quality data. A summary of the oil degradation parameters that are accessible using the FluidinspectIR ™ product line is presented in Figure 6.

Apart from these more conventional parameters, it is often also possible to measure parameters that are not commonly reported in a conventional oil analysis report but might provide an interesting insight into a specific application.

One example is the emulsifier absorption by the lubrication gear oil following a water leak during steel production.

The FluidinspectIR™ sensor platform can be complemented with additional sensors, such as viscosity, optical particle counting, etc., to provide customers with a complete solution for their respective applications.

Turbine Application Case Studies

The following shows where a FluidInspectIR™ system has been installed on a gas turbine in Figure 10. Field results from this application over the last nine months show lab results that are indistinguishable from real-time sensor results.

In this instance, the customer aimed to track not only phenols, amines, and oxidation levels in their oil but also the concentration of Solvancer™, a product used to enhance solubility and provide long-term protection against varnish.

Figure 8 illustrates that the average Solvancer level is maintained at around 3.09%, with the ability to detect changes as small as 100ppm. The Solvancer concentration must be held at a specific concentration over time, and it is crucial to monitor this level accurately during the turbine’s operation.

The FluidinspectIR sensor monitors the overall oil condition remotely and can identify any changes in concentration, such as when new oil is added to the system.

The fresh oil would dilute the Solvancer, resulting in a noticeable decrease in its percentage, potentially accelerating the varnish production processes of the lubricant. Based on the observed drop in the Solvencer concentration, the customer can be advised to top up the required concentration to retain optimum lubricant protection.

The most innovative aspect enabled by accurate, lab-quality real-time data for turbine and/or other asset management applications is “The Power of Slopes.”

The FluidinspectIR provides a complete oil analysis every hour, which enables us to analyze the slopes for any given parameter rather than look at the absolute values, which is the status quo, using conventional oil analysis.

An example of a brand-new diesel engine that suffered extensive soot generation during operation is shown below. After 200 hours, the customer decided to change a crucial component in the engine, which was the suspected root cause of the soot problem.

At the same time (220h), the oil was also changed, as is evident by the drop in oxidation (red circle at the bottom image).

Comparing the slopes of the soot graph before and after the maintenance process, it is evident that the root cause of the problem was not the suspected part, as the slopes are identical. The sensor data provided this information after 40 operational hours.

The same approach is now being employed in turbine oils. Figure 13 displays a pattern where the oxidation rate in the turbine oil progressively increases across three oil change events. This trend could indicate a shift in the turbine operation or duty cycle.

However, it is more probable that the oxidation rate increased due to insufficient cleaning of the lubrication system between each oil change. Typically, the lifespan of oil is reduced by half if the cleaning between oil changes is not thorough, and the data presented in Figure 9 aligns with this understanding.

In the same way, the sensor data can now be used to ensure that any potential adverse effects related to an oil change are minimized, as the changes in the slopes will be evident even after a few days of operation.

Figure 11 illustrates a noticeable shift in the oil’s concentrations of phenols and amines. The in-service oil was a phenol-only formulation. However, the graph shows a significant decrease in phenol levels coinciding with a marked rise in amine levels.

This suggests that a new formulation, different from the original phenol-only composition, was introduced into the oil. This change can serve as an alert, allowing for a prompt investigation to confirm whether the addition was deliberate and whether the new formulation is compatible with the existing system.

Almost every turbine component is monitored in real-time, except for its lubricating oil. As the operational conditions of rotating equipment and turbomachinery evolve, there’s a reduction in on-site resources to support traditional lab-based condition monitoring.

This contrasts with the growing desire of central turbine engineers to get instant overviews of their entire fleet’s performance. These trends highlight the ever-increasing necessity for sensors that can provide real-time monitoring of the oil’s condition.

The first step in choosing a suitable sensor involves understanding the machinery’s and oil’s potential failure modes. For rotating equipment, the main concern is bearing health, particularly the oil’s tendency to form varnish.

Key changes to monitor in the oil include the depletion of antioxidants, corresponding reduction in oxidation stability, and an increase in oxidation, which leads to a higher potential for varnish formation.

Currently, mid-infrared technology is the only sensor capable of measuring these crucial failure modes. Notably, the data quality from these sensors matches that of laboratory tests, eliminating the need for new correlation studies to interpret the sensor data.

Spectrolytic’s FluidInspectIR has been effectively implemented in turbine settings, showing significant potential in fulfilling the increasing demand for accurate, affordable, real-time monitoring of some of the world’s most critical assets.

References

[1] Kazempour-Liasi, H., Shafiei, A. & Lalegani, Z. Failure Analysis of First and Second Stage Gas Turbine Blades. J Fail. Anal. and Preven. 19, 1673–1682 (2019). https://doi.org/10.1007/s11668-019-00764-1

[2] “Steam-turbine diaphragm repair strategies” https://www.ccj-online.com/steam-turbine-diaphragm-repair-strategies/

[3] John K. Whalen, Thomas D Hess Jr, Jim Allen, Jack Craighton. Babbitted bearing health assessment, Middle East Turbomachinery Symposium 2015.

[4] Mathura, S. “Lubrication Degradation Mechanisms (Reliability, Maintenance, and Safety Engineering),” CRC Press, 2020

[5] Livingstone, G., Rista, E. “How to Select Turbine Oils Strategically for Improved Results Now,” https://precisionlubrication.com/articles/select-turbine-oils/

Most industrial oils, when new, have a clear, translucent color. This typical color is maintained for a period while the product is in its original container and free of harmful effects. However, once the oil is put into service, it undergoes changes that are initially chemical due to factors such as the operating temperature, the operating environment, the conditions of the equipment, the content of the tank, and the system where it works.

The first noticeable change in the eyes of the lube technician is the color. A high percentage of oils undergo a color change after a few hours in service, and as the service life of the oil elapses, the color may move further away from its original state. What is this color change due to? Is it normal for the oil to change color? When is the change of color an indicator of a problem?

These are some questions that many lubrication and oil analysis managers have asked themselves at some point and generate many doubts.

The picture below perfectly describes the doubts suffered by the technician in charge of lubrication in the first instance and the proper management of lubrication later.

The sample on the left was taken from the free side bearing, while the sample on the right is from the coupling side bearing. The color of both samples is entirely different to the naked eye, but what is the difference between the two?

Laboratory Color Analysis and Current Alternatives

One of the most common routine tests in commercial laboratories is the application of ASTM D1500 (Standard Test Method for ASTM Color of Petroleum Products), where the sample is visually compared against a standard, but it is still a subjective comparison.

The difference is evident in the examples in the photo above, and the samples receive different classifications. However, the doubt remains; Do any samples have signs of deterioration?

Alternatives vary by lab and often range from simple definitions like “dark” or “light” to more precise ones where the lab analyst compares the sample against a color palette.

Although these methods have a procedure, the margin of error is usually large since, for one technician, the sample may be “dark.” In contrast, for another technician, the same sample may be “slightly dark.” We no longer consider the variation that it happens on Monday mornings or Friday afternoons, where the eyes seem to be more tired to opt for a good definition of the color type of the sample.

What Can a Color Sensor Contribute?

I had the opportunity to grow professionally in a research center where we had a lot of resources and knowledge. This allowed me to satisfy many of my doubts and conjectures about what happens to lubricants. Among these, that color changes.

Why does the color of an oil change after a few hours in some cases, while in others, it can remain unchanged for years? What chemical implications does the color change have? And even more critical from an operational point of view, is the color change an indicator that the oil requires corrective maintenance action?

In 2016 I worked on designing a color sensor that could identify those small changes. Although it was the first big step to understanding what happens when the oil changes color, it was imminent that it needed to rely on other techniques to validate what was happening with the oil.

This sensor traversed the visible spectrum, simultaneously comparing each measurement against two different sources. A previously loaded blank and the darkest color were recorded throughout the measurement.

The sensor made 800 to 1,200 measurements of a sample in about 4 to 9 seconds, depending on the translucency of the sample.

Once all the points had been analyzed, the software allowed a value or a range to be given in some cases, which could be later worked on, analyzed, and compared for more specific purposes and to determine if the color indicated a change that could harm the oil.

Applicability

Rationally the main limitation of color sensors is the opacity of the sample. Very dark samples do not emit significant results that can be contrasted or are effective when determining the state. This is mainly because the sample entirely absorbs the light beam emitted by the sensor and does not emit any pulse that can be compared or analyzed.

Thus, this type of sensor has a much broader application in industrial oils such as hydraulics, gears, turbines, and compressors.

The sample analysis software allows obtaining results in any of the following options:

1. Chromatic scale
2. Achromatic scale
3. A combination of the two above
4. CIElab color

Turbine, compressor, and hydraulic oils are initially translucent and clear, but as they change chemically, they tend to darken from the point of view of our sense of sight. It was on a group of nearly a thousand samples of these types of oils that the color study focused on.

Like any determination of parameters, it is necessary to have a reference to recognize, identify and control that the physical changes are due to a chemical condition. For this case, the acid number (TAN), metal concentration, and measurement by infrared spectrum were used. (FTIR).

On the other hand, the operating conditions were also considered in this case: oil temperature, ambient temperature, ambient humidity, the concentration of water in oil, and hours in service.

Initial Results

Temperature

Temperature is the main factor for the drastic change in the color of the oil. Oil that exhibits color changes induced by temperature effects tends to be reddish amber, and as it remains in service, the reddish color tends to darken further.

The previous case corresponds to a synthetic oil in a bearing that, at 4450 hours, presents a dark color and with a TAN concentration of 15 times the initial one. The oil was kept in service for ~400 more hours until it was changed.

Generation of Chemical Compounds

The chemistry of lubricants is very complex, and, in many cases, neither the mode of formation of certain compounds nor the way to eliminate them has been determined. The acids measured in oil analysis encompass many of these compounds, but logically not all of them, nor does it specifically detail what acids these are, nor does it define whether, in the medium term, they could transform into other, even more harmful compounds.

In the following case, we will see that the measured levels of AN are very similar.

However, compounds such as succinimides and lactones (~1700 cm-1) are clear indicators of chemical changes that will lead to harmful products for the oil and the active ingredient later.

This case is very typical in gas turbine and compressor oils with varnishes that affect the availability of the asset. Despite carrying out constant filtration to eliminate the varnish – with the expense that this entails – it is impossible to stop the origin of the problem, which is the loss of a compound that, for the reader’s better understanding, we will define as an additive! From the base!

This compound has been reduced to such an extent that it cannot release the air trapped by the agitation of the fluid in high-pressure areas, generating implosions that crack the lubricant, forming organic by-products that will later be measured as varnishes. Of course, only a handful of laboratories in the world can determine the origin of the problem. Measuring the varnish is simple; determining its origin is another matter.

Presence of Water

Water is a common compound in lubricating oils, not only because they are very similar to it but because it is part of its chemistry, and depending on the type of oil, it may contain more or less water molecules.

Fortunately, just as it is easy to retain water, its physicochemical characteristics can also include it without showing drastic changes. However, one of the most aggressive effects is due to hydrogen and not water, but we will talk about the effect of hydrogen in another article.

This sample corresponds to synthetic oil in a bearing from a plant with a high presence of process water. Although acid generation is not extreme (low TAN), the presence of water can change the color of the oil as both are in service; This is what I mean by the constant movement or agitation that the oil-water fluid undergoes.

At the end of its service life, the oil tends to show a dark greenish color, and, in some cases, it is associated with the formation of sludge with a low acid concentration. On the other hand, a high acid concentration (high TAN) in the presence of water can generate colors that, to our eyes, tend more toward the yellow spectrum. In appearance, they tend to be lighter rather than darker.

The color change is a conclusive indicator of a chemical process that gave birth to a physical change in the oil. It is not always bad, but it is necessary to understand what the color change implies since each machine-oil unit dances to its own rhythm. Do you understand the rhythm of your assets?

Oil analysis on oil reservoirs, such as a paper machine, provides much more value than most maintenance managers realize.

Regular reservoir oil sampling can provide the following:

• Reactive information, such as a spike in wear metals identifying a failed bearing or another component.
• Predictive information, such as a slight increase in wear metals indicating early stages of an impending failure.
• Or, most valuable of all, it can provide proactive information. Oil analysis can tell you when your additives are getting depleted, contamination levels are getting to a point where additional filtering may be needed, moisture levels are to the point where corrective measures are required to prevent damage, and with enough data, may even tell you if your equipment is operating outside of its intended design (too hot, too slow, cavitation).

It’s all about using facts and data to help us make better maintenance decisions and get the most out of our investments. Improving the life expectancy of equipment, extending the life of oil life, and reducing unplanned downtime (which also improves safety) are just a few reasons the value of oil analysis far outweighs the cost.

So, would continuously monitoring the oil add more value if oil analysis provides that much value? We recently decided to add a continuous oil monitoring system to the main lube on our paper machine lubricating system.

We then took that information and sent it to our historian to be trended 24/7. We are trending the oil temperature, the moisture level, and the ISO 4/6/14 particle counts.

When we started this project, we did not realize how fast it would pay for itself! Shortly after getting the system online (but before trending), the maintenance manager notified us that the paper machine dryer head had developed a steam leak.

We watched the oil condition and saw no change. A few days later, the moisture levels began to climb rapidly. Thanks to the monitoring system, we reacted to this issue by periodically draining some oil and hooking up a vacuum dehydrator.

Within 24 hours, we reduced the moisture to an acceptable level and returned to normal within 72 hours. No doubt, this quick response minimized the amount of damage the water in our system caused.

Nicholas Knott, a colleague at another paper mill, had the same experience while trending moisture levels in his paper machine. He also caught it quickly and believes this technology prevented many bearing failures. Below is the trend showing how quickly the moisture reaches unacceptable levels and how fast his team could resolve it.

The lesson from these unfortunate situations is that if we were not doing continuous monitoring, how much damage would’ve occurred? We will never know, but we know that steam leaks on a dryer head were common in the past, and oil analysis was rare.

We also know that we had to replace many bearings in the past, which is rare now. Using continuous oil monitoring systems may only be necessary for some situations. Still, in a challenging application like a paper machine, it adds way more value than it costs.

Soon we expect to have enough data to understand if situations like start-ups, shutdowns, wash-ups, and machine speeds have impacted our oil quality.