Oil analysis is critical to any reliability program, serving as an effective predictive tool for potential equipment failures. When properly deployed, oil analysis has the power to enhance maintenance practices within industrial and fleet settings.

This article will review the benefits of onsite oil analysis and demonstrate how Key Performance Indicators (KPIs) are used to select the right instrumentation to support an onsite lab and effectively implement oil analysis data.  By doing this successfully, organizations can reduce maintenance costs, increase equipment reliability, and align operational strategies with financial goals.

The Benefits of Onsite Oil Analysis

Onsite oil analysis eliminates the delays associated with sending samples to external laboratories. However, before deciding to bring a program onsite, users must consider if the expertise and manpower are available to support onsite tasks.

Even if these items are available, solutions must still be user-friendly, efficient, and capable of delivering actionable data. When these items are in place, rapid analysis facilitates immediate decision-making and empowers maintenance teams to customize reporting based on real-time data.

Key Performance Indicators (KPIs) in Maintenance

KPIs are essential for measuring an organization’s effectiveness in achieving its objectives. In the context of maintenance, KPIs are typically linked to financial goals. Some common examples of maintenance-related KPIs could be:

• < 20% abnormal results in wear particulate
• Reduce oil consumption by 25%
• Extend bearing life by 6 Months
• Increase production by 25%
• Extend oil change intervals by 6 months on 50% of the fleet.
• Zero failures on machines categorized as “critical”

Determining Which Oil Analysis Data Points Match KPI Objectives

Effective oil analysis hinges on trending the right data. Before reaching the end goal of selecting the right instruments, let’s use the KPIs above as examples to determine what kind of data points are needed to trend those KPIs:

Now that the right data points are determined.  The proper instrumentation can be selected. Building on the table above, let’s make some onsite instrumentation recommendations:

Another useful resource for selecting the right instrumentation is Ametek Spectro Scientific’s product selector guide: https://info.spectrosci.com/product-selector.

It’s important to note that many of these recommendations can be built upon over time.  An easy place to get an almost immediate return on investment is focusing on lowering particle counts and moisture control. By establishing a solid business case for oil cleanliness, organizations can justify initial investments in filtration equipment, breathers, or additional instrumentation.

Bringing it All Together: Software for Data Management

Now that the proper data is in place, having an effective way to trend the data and report the results becomes critical for continued success. The software should be easy to navigate and allow for quick implementation into the reliability program’s workflow. 

Ametek Spectro Scientific offers the TruVu 360 Fluid Intelligence software that pairs with both the MiniLab and FieldLab onsite systems.  The software plays a pivotal role in managing and interpreting oil analysis data. Some key features include:  

• Performance dashboards that allow users to visualize data trends and KPI tracking.
• Recommendations for alarm limits based on ASTM D7720 statistical analysis.
• Automatically populated diagnostic statements and alarm limit sets to enhance the ability to identify and respond to potential issues effectively.

Establishing effective KPIs and utilizing data-driven insights from onsite oil analysis can lead to substantial cost savings and operational improvements. Organizations must take the time to align their oil analysis data with primary financial goals and leverage software solutions to document successes to justify and ensure longevity of the maintenance program.  

References

https://www.machinerylubrication.com/Read/30/oil-analysis-benefits

https://reliabilityweb.com/articles/entry/measuring_plant_performance_-_the_need_for_metrics_standardization

https://www.spectrosci.com/product/minilab-153

https://www.spectrosci.com/product/truvu-360

https://www.astm.org/d7720-21.html

Examining oil analysis programs – both off-site and onsite programs across various industries- it is observed frequently that oil analysis abnormal alarm observations and recommendations are not being acted upon promptly.

The ability of reliability teams to ignore the results is primarily due to a lack of trust in the triggered alarms. This behavior is known as Alarm fatigue. First identified in the medical community, Alarm fatigue is a sensory overload condition where technicians are exposed to excessive alarms, which can result in desensitization and missed alarms.

An immediate issue is the identification of the systems needing the most attention. If everything is in alarm, how do you prioritize? In facilities where workers multitask and already have significant task backlogs, ignoring or failing to respond appropriately to such warnings can lead to unplanned downtime.

This article outlines why the condition occurs and a process to intelligently set alarms using new TruVu 360 Fluid Intelligence Platform features for onsite MiniLab and FieldLab analysis.

Understanding Alarm Settings for Oil Analysis

Oil analysis alarms may be set in several ways: rules of thumb, rate of change, or OEM / Lubricant supplier guidance. However, when starting up a new program, most reliability engineers opt to focus on OEM limits. OEM guidance is focused on best practices, usually on optimum equipment design duty cycles.

The challenge arises when the equipment duty cycle, lubricant type, or ability to change results from lack of repair ability (e.g., no filtration systems) leads to “normal” conditions with oil analysis measurements considered abnormal with these OEM values.

False alarms can be very problematic. False positive alarms may be triggered when severe level alarms are set too low. A conservative approach to setting alarms at the start of a condition monitoring program (e.g., OEM limits or generic industry values) can create false positive alarms that, when presented to equipment operators, lead to a lack of confidence in the capability of the oil monitoring program.

Today, oil analysis programs have more data available than ever to inform lubrication and reliability strategies. An effective lubrication program can return a 40:1 return on investment; however, this is only possible when oil analysis programs are trusted, and more specifically, the alarms and diagnostics are appropriate for the facility and machine being monitored.

Keeping Control

In any organization, the pareto based principle is assumed in a controlled facility. For oil analysis programs, the case is similar, whereby a majority of equipment should be in “normal” operating mode, and a percentage (typically around 20%) is what requires attention. Usually, sample results are color-coded yellow for caution and red for severe so the owner can manage the assets that need the most attention. This has a practical benefit in any facility where assets are working and resources are limited.

When a facility begins to have a significant amount of alarms greater than 20%, it becomes challenging to manage, and this is where alarm fatigue develops. The reliability engineer has more assets that need attention than resources available. So now what?

Figure 4. An oil analysis program where alarms are not set for practicality

Looking at the example in Figure 4, it is nearly impossible for a reliability engineer to manage a program when ~ 84% of the equipment is in severe alarm. If addressed at all, these alarms will likely be ignored until the next sampling.

Determining Better Alarms Using Historical Data

Determining better alarms and avoiding alarm fatigue can be achieved by performing a periodic statistical analysis, evaluating and adjusting alarms based on ASTM D7720-21 Standard Guide for Statistically Evaluating Measurand Alarm Limits when Using Oil Analysis to Monitor Equipment and Oil for Fitness and Contamination. ASTM D7720-21 defines statistical techniques for evaluating whether alarm limits are appropriate for flagging problems requiring immediate action.

The methodology and mathematics can be developed with Excel or other software; however, it involves exporting data and some effort to generate information. A new capability called Condition Based Alarms (CBA) based on ASTM D7720 is now available in TruVu 360, the fluid intelligence platform for onsite MiniLab and FieldLab systems at Ametek Spectro Scientific

The statistical evaluation using D7720 can be applied to all existing components within TruVu 360. A historical sample set of at least 80 samples within a component is required to complete the calculation. Using the guidelines outlined in the ASTM D7720, TruVu 360’s CBA feature produces an output that helps the user:

• Evaluate current alarm limits in each limit set to determine if current alarm limits are effective.
• Adjust alarm limits per limit set based on historical data (>80 samples needed).
• Evaluate the effectiveness of current alarms vs recommended alarm limits.
• Carefully and systematically develop an alarm strategy that is achievable and sustainable.

Using Condition-Based Alarms to Manage Steam Turbines in a Refinery

Steam turbines are widely used to drive process trains in refineries. It’s not unusual to find older turbines that can tolerate high levels of water and particulate. In many cases, these same turbines are assigned alarm profiles with strict limits for particle counting and water. Routine monitoring can result in a series of false positives that maintenance personnel ignore.

A recent review of an oil analysis program at a Gulf Coast refinery showed that many of its steam turbines (93 of the 83 assets) were classified as abnormal or severe. Furthermore, the TriVector distribution indicates that 70% of the abnormal or severe alarms are a result of contamination and wear present in the oil.

Table 1 shows an example of evaluating the existing alarm set vs. the proposed new alarm set to help understand how the CBA statistical analysis of alarm limits is used within TruVu360.

Table 1. Statistical analysis (per D7720) of refinery oil sample data from steam turbine.

Particle Contamination

Most reports for the steam turbines were alarmed due to the high particle count. CBA analysis confirms this and provides a new suggested alarm limit that, if implemented, will only alarm the top 3% of samples.

The graph in Figure 6 presents CDF (Cumulative distribution function) plot of ISO 4406 Code >6 measurements. The steam turbine’s current severe limit for ISO Code >6 is 15, and the suggested recommendation based on the CBA analysis severe limit is 27.

With the CBA approach, it is easier to prioritize the effort based on the worst samples in the refinery first, which are identified by the top 3%.

It will take a consistent long-term contamination control program initiative to achieve the refinery’s current ISO 4406 Code limits. But, with step-level initiatives and [i]focusing attention on a portion of the assets to demonstrate success, The expected payback will be improved machinery health, asset life extension, overall reliability improvement, and substantial cost avoidance. [ii]

Mechanical Wear Parameters and Alarm Limits

The above example is easy to understand, as there is widespread awareness and publications of ISO codes for contamination control, and most reliability engineers can reset values based on their comfort zone.

However, many powerful wear debris analysis parameters have been introduced in the last ten years for condition monitoring that do not have much OEM detail and experience. Figures 7 and 8 present a distribution of refinery steam turbine measurements using two wear parameters: Ferrous Wear Severity Index (FSWI) and Total Ferrous. Both measure severe ferrous wear.

The steam turbine’s current severe limit for FSWI is 4, and the recommended severe limit is 76. The steam turbine’s current severe limit for total ferrous is 20, and the recommended severe limit is 42. Samples exceeding severe alarm limits for FSWI and Total Ferrous must be explained and never ignored. Consider increasing these severe wear limits from 85th to 97th percentiles to prioritize limited resources and focus on the most serious wear indications.

 Alarm fatigue can prevent lubrication programs from making small improvements over time. However, the concept of condition-based alarms can help users avoid this. By carefully and systematically using data to drive alarm limits, the user can set more effective and practical alarms that can sustain maintenance efforts year after year.

For more information, please visit https://www.spectrosci.com/product/truvu-360.

References

[i] “Challenges and Solutions in Implementing a World-Class Lubrication Program,” Machinery Lubrication

[ii] Annual Cost Savings From Effective Lubrication Programs With Onsite Oil Analysis, Spectro Scientific, https://blog.spectrosci.com/annual-cost-savings-from-effective-onsite-oil-analysis

Onsite oil analysis is an effective tool to analyze samples and optimize maintenance activities quickly. As part of a comprehensive condition-based maintenance program (CBM), oil analysis effectively complements other diagnostic technologies like vibration analysis, infrared thermography, and ultrasound technology.

However, this critical lubrication monitoring step is often overlooked in grease-lubricated equipment. SKF states that 80% of the world’s bearings are grease-lubricated, leaving a vast opportunity to incorporate grease analysis techniques into the overall CBM strategy.

The Electric Power Research Institute (EPRI) suggests that nearly 50% of bearing failures are related to poor lubrication and contamination¹. The development of grease condition monitoring standards, ASTM D7718 and ASTM D7918 have laid the foundation for a consistent methodology to sample and test grease to implement condition monitoring strategies.

By monitoring key data points, such as wear, oxidation, and additive health, the asset manager can transition from calendar-based to condition-based changeouts. This has the potential to save hundreds of thousands of dollars per year for an owner of a large fleet.

The wind, rail, and automotive robotics industries are implementing these strategies into their programs and potentially avoiding thousands in maintenance costs by predicting failures and extending greasing intervals³.

Historically, incorporating any CBM strategy into grease-lubricated components has been challenging.

The small quantity of grease typically available on an in-service component and the limited testing available for small amounts of grease often present barriers to routine grease sampling and analysis.

To address these challenges, grease sampling tools can capture a representative sample from bearings and gears requiring as little as one gram of grease. Onsite analysis tools are available to assess the wear and physical properties of the grease.

This simple sampling technique can be used in various industries, including but not limited to wind, rail, robotics, mining, and nuclear, to sample a large number of grease-lubricated components and evaluate the next actions based on the criticality of the data.

Periodic sampling and analysis of the grease from these components can give asset owners a clearer picture of equipment health, determine grease conditions for optimal change-out periods, and pinpoint latent issues that can be addressed before failure.

Grease Sampling

In most circumstances, procedures for obtaining grease samples from bearing housings and gears are not consistent and likely do not represent the actual condition of the “active” grease near the lubricated surface.

Therefore, the challenge in optimizing a grease analysis program is developing test methodologies that measure in-service grease conditions utilizing a small amount of grease and a sampling process that enables representative grease samples to be taken without disassembly of the component².

While it is a reality that the user may need to scoop and scrap to the best of their ability to get a sample, there is a standard method that exists for taking in-service grease samples: ASTM D7718 Standard Practice for Obtaining In-Service Samples of Lubricating Grease.

This standard shows several standard methods to take a representative grease sample and utilizes the Grease Thief® sampling device to sample the grease from a bearing, valve, or gearbox.

Grease Thief

Depending on the instrumentation being used, some scoops or tubes may be available with the instrumentation. Speak with your instrument manufacturer and ask which tools are available to sample and measure grease within the instrument. But know, there are standard methods out there to be utilized.

Grease Analysis as a Screening Tool for Fleet Analysis

Routine grease analysis is common in high-value fleet applications such as locomotives, automotive robotics, and wind turbines, where a relatively straightforward set of screening tests for wear and oxidation can guide grease relubrication frequency, cases of mixup, and monitoring wear levels.

Bearing or joint failures could result in millions of dollars in lost product or power, or even worse, could threaten the safety of employees and customers. Asset owners must be able to look at a large quantity of data and pinpoint latent issues where they can focus their resources and prioritize accordingly.

Compared to other diagnostic technologies, grease analysis can detect issues earlier on the P-F interval than vibration analysis, allowing asset owners more time to address the problems and avoid potential downtime.

Once a representative sample is taken, onsite monitoring of the grease sample can be done using existing onsite equipment that was likely purchased for oil analysis.

Onsite Tools for Grease Analysis

Ferrous monitoring tools, handheld infrared spectrometers, and metals spectrometers can be used to measure ferrous debris, physical properties, and contaminants present in the grease. These onsite tools allow for quick monitoring of many samples so immediate action can be taken.

Ametek Spectro Scientific offers a variety of onsite tools like the Ferrocheck, FluidScan, and Spectroil M or 100 that can be used for both onsite oil and grease analysis:

Ferrous Debris Monitoring with the FerroCheck

Ferrous debris monitoring is the most common and cost-effective way to trend wear issues on bearings, gearboxes, or valves. The FerroCheck is a magnetometer that senses the disruption of the magnetic field due to the presence of magnetic particles in the grease.

The particles’ disruption can be directly correlated to the amount of ferrous debris in the grease. The FerroCheck provides a quick, straightforward, non-destructive solution for measuring the ppm of Iron in grease⁷.

Ferrocheck

Based on the criticality of the component, the sampling frequency can be determined so wear trend analysis and alarm limits can be determined. It’s important to understand that wear particulates in grease are cumulative and, unlike oil, wear particles will remain in the grease until deliberately purged or flushed from the component.

With the ability to detect up to 15% ferrous wear, the FerroCheck is an effective tool for trend analysis and can identify outliers in a fleet application.

Infrared Spectroscopy with the FluidScan

Utilizing the FluidScan (compliant with ASTM D7889), Infrared Spectroscopy is a powerful tool that can be used onsite to measure grease oxidation and identify potential contaminants like moisture or mixtures with other greases.

Monitoring these parameters via trending and direct property analysis makes it possible to inform the user when the grease has completed its useful service. Using a comparison library (over 800 oils and greases), FluidScan can compare the grease sample to the reference to identify potential grease mixing.

If possible, mixing greases is a practice that should be avoided. Mixing of greases can lead to changes in the rheological properties of the grease and eventual separation of the oil from the thickener. If considering mixing two greases, it is best to perform a compatibility study (ASTM D6185) to determine if mixing is acceptable.

Moisture and oxidation can also be determined on the FluidScan. A typical moisture peak can appear on the IR Spectrum around 3400cm-1. As these greases age and oxidize, the buildup of oxidation products may be monitored by the FluidScan infrared analysis.

This is translated into an oxidation value and water index. It’s important to note that some polyurea-thickened greases also have a peak on the IR spectrum in this region, and care should be taken not to mistake this peak for moisture.

The polyurea peak at 3400cm-1 will be a short, small peak versus a moisture peak, which will be a larger, broader peak. These same IR masking issues can also occur in greases formulated with an ester-based synthetic base oil.

These greases will show a peak at 1750cm-1, where oxidation also appears. It’s essential to understand when these greases are being used and note this could impact the oxidation and moisture trend values.

As with any effective CBM program, it is important to establish trends and focus on how the grease deviates from the trend. Any significant deviations from the trend would require action. Over time, alarm limits can be established for particular components based on equipment load, runtime, and environmental conditions.

Spectroil M and 100 Series Spectrometer

Using a Rotating Disc Electrode (RDE) Spectrometer, the concentration of metals in the grease can be compared to the new grease to identify significant differences in additive metals that could point toward grease mixing.

Also, the presence of wear metals (Iron, Lead, Tin, and Copper) can be determined. RDE Spectroscopy has been a common laboratory and field method for quick grease analysis over the last 15 years. Sample preparation is important; however, it differs based on early adopter experiences.

The two most common preparation methods are dilution (slurry) and wet smear. In the case of dilution (slurry), the grease sample is diluted with a solvent to create a low-viscosity slurry that may be placed in the sample cup and excited as normal.

A second method is the smear (wet) approach whereby a rotrode is rolled in the grease sample to create a coating on the disk edges, and then it is mounted on the shaft, and a sample cup of base oil is used. Either method relies on the consistency of the operator and an understanding of the goals.

The tests and instrumentation listed here for in-service grease testing are not exhaustive.

For a complete list of recommended tests, consider reviewing ASTM D7918 Standard Test Method for Measurement of Flow Properties and Evaluation of Wear, Contaminants, and Oxidative Properties of Lubricating Grease by Die Extrusion Method and Preparation, which discusses additional points to monitor like moisture, color, and consistency. For more information on adding those tests to your grease analysis program, contact MRG Labs.

Acknowledgments & References

Special thanks to Rich Wurzbach, MRG Labs, for sharing his grease analysis knowledge with me over many years.

[1] E. (2001, October). EPRI_Lubrication Guide 1003085. Retrieved from https://www.scribd.com/doc/128347108/EPRI-Lubrication-Guide-1003085

[2] Williams, L. A., & Wurzbach, R. N. (2016). Managing the Health and High Costs of Robotics Using Grease Sampling and Analysis (Tech.). Hot Springs, VA: NLGI National Meeting.

[3] Kowalik, G., & Janosky, R. (2017). Advancements in Grease Sampling and Analysis Using Simple Screening Techniques (Tech.). York, PA: MRG Labs.

[4] Williams, L., Wurzbach, R., & Alarcon, J. (2016). Integrating Grease Sampling and Analysis into Wind Turbine Maintenance Programs (Tech.). Bilbao Spain: LUBMAT.

[5] https://www.awea.org/wind-energy-facts-at-a-glance

[6] McKenna, P., Subramanian, M., Berwyn, B., Kusnetz, N., Jr., J. H., Lavelle, M., . . . Spiegel, J. E. (2017, June 01). U.S. Wind Energy Installations Surge: A New Turbine Rises Every 2.4 Hours. Retrieved from https://insideclimatenews.org/news/03052017/wind-power-rising-clean-energy-jobs

[7] Oil Analysis Handbook (3rd ed.). (2017). Chelmsford, MA: Spectro Scientific.

For years, rig operators have used oil analysis as an essential tool for routine maintenance and future cost avoidance. On oil rigs, equipment failures risk employee safety, and missed production targets are realized very quickly. The remote locations of offshore rigs make routine maintenance very expensive.

In most cases, skilled people and supplies can only reach the platform by ship or helicopter, so the cost of bringing technical specialists, replacement equipment, spare parts, and tools is high.

Oil analysis is critical in alerting the maintenance team to problems that may damage a vital system. An effective oil analysis program also helps to allocate scarce resources efficiently by planning maintenance based on equipment conditions rather than time intervals.

Currently, most offshore platforms take oil samples and ship them by helicopter to onshore labs for analysis. Once the results are available, they are sent back to the platform.

It costs more than $1 billion to operate a typical production platform over its 10 to 20-year life cycle, so operating costs per day can be estimated at $100,000 to $300,000.

A typical offshore platform contains millions of dollars of machinery critical to the crew’s safety and whose failure can easily put the platform out of operation. Oil analysis determines the amounts of various metals in the oil, providing a fast and inexpensive way to gauge the machinery’s wear.

Oil analysis also helps determine the condition of the oil by measuring byproducts formed by oxidation and by measuring the viscosity. Monitoring oil condition reduces the risk of catastrophic failure and the high cost of changing and disposing of oil in heavy machinery.

Typical equipment sampled on an offshore rig:

• Turbo gas-powered generators
• Fire water pumps
• Diesel engines
• Gearboxes
• Pumps
• Crane engines
• Hydraulic systems

Value Of Oil Analysis on Offshore Platforms

Recently, an offshore rig in the Gulf of Mexico contacted a reliability service provider in Louisiana to seek a solution to improve their oil analysis program. The long turnaround time from their onshore lab compounded the weaknesses of their current program.

Key Issues to Address:

• Frequent resamples due to mislabeling. Collected samples from the various rotating equipment on the platform were misidentified, leading to erroneous results by the contract lab. Policy demands action to CAUTION or ALERT status samples, so long delays caused operators to discard results.
• Missed Compressor failure A genuine problem detected by onshore oil analysis on a failing air compressor was received one month after the compressor had failed without warning. The rig operator then incurred extra costs replacing it. It can cost $250K for expedited freight and installation charges to replace a critical system ASAP.
• Resources for onsite oil analysis. The rig operator had submitted their operating budget for 24 months to the oilfield license holder. They could not dedicate rig personnel to operating onsite oil analysis equipment in addition to sampling.

The Solution: FieldLab C

The oil producer asked the reliability service provider to travel to its platforms with a portable oil analyzer that could provide comprehensive analysis, deliver immediate results, and generate a full report with recommendations throughout the shift.

The service provider proposed sending a Level III Vibration Analyst/Tribologist Service Engineer, who had all the safety certifications for transport and was familiar with rotating equipment and maintenance strategies on board the rig.

The major challenge was to select a portable, solvent-free oil testing device that could measure abnormal metals, viscosity, oil chemistry, and particles with the ability to provide a report. The solution chosen was the FieldLab system.

When deploying field solutions, careful consideration must be given to turnaround time, technique, and data integrity. The FieldLab onsite oil analysis system provides fast results with the data integrity of a commercial oil analysis lab.

The FieldLab is a rugged, portable expeditionary fluid analysis system that allows operators in the field to perform comprehensive, mobile lubricant sampling. The battery-powered device enables complete lubricant assessment for condition monitoring and rapid results that permit informed maintenance decisions.

It requires only a small sample volume to perform a comprehensive analysis, and no chemicals or solvents are needed for sample preparation or cleaning. With all four tests, the complete analysis only takes about 5 – 7 minutes.

FieldLab is a unique system design where machine failure and root cause analysis are interpreted using a comprehensive process combining a modified pore blockage particle count technique with an XRF Analyzer.

This unique system design allows for a broader range of particle size detection, allowing the user to identify changing wear rates and isolate potential root causes of problems in lube systems. Additionally, viscosity and oil chemistry can be run simultaneously.

Initial Examples

There have already been several cases where the savings from onsite oil analysis exceeded the entire year’s service cost.

For example, technicians on one platform replaced the diesel engine on a crane. When the technician visited the platform and tested the oil, the viscosity was 70 cSt when it should have been 120 cSt. The technician ran additional tests and discovered the presence of diesel fuel in the oil.

A mechanic put dye in the fuel supply and found a broken injector line was leaking diesel fuel into the oil sump. This leak could damage the engine or cause a fire. The oil analysis results made it possible to fix the problem with a low-cost solution – replacement of the injector line.

In another case, the oil analysis results on a large gas turbine compressor showed a high particle count. The technician queried the platform’s maintenance team and discovered they had recently replaced a valve in the lube oil system.

The technician wondered whether the oil particle count might have spiked in response to this maintenance, so he flushed the lube system and ran another test. This time, the test showed a much lower particle count, although still above normal values.

After discussing the situation with the maintenance foreman, the decision was made to do nothing immediately but to retest the equipment the following month. The particle count had returned to normal levels when the equipment was retested.

According to the maintenance foreman, if an onshore lab had tested the oil, there would have been no chance to do an immediate follow-up study. It would have been necessary to, at the minimum, perform vibration testing and possibly perform even more expensive repairs.

Data Management and Reporting

The results of offshore oil analysis can be uploaded to the TruVu 360 Fluid Intelligence System. The results are available not only to the maintenance team on the platform but also to onshore managers and analysts who track trends and provide recommendations on whether to invest in a particular piece of equipment.

The oil analysis method used on most offshore platforms takes up to a month to send samples to a lab and receive the results.

Onsite oil analysis has the potential to provide significant value and benefits by allowing testing on the rig and delivering immediate answers to the maintenance team. Faster results can prevent breakdowns and avoid unnecessary maintenance.

The savings are realized by getting answers quickly and better managing equipment uptime on the offshore rig.

Implementing reliability solutions involves solving problems and deciding how to effectively use oil sample test data as a fundamental building block to drive program success. Many decisions confront the end user when they perform their own oil testing and analysis.

This article provides seven elements to consider when going through the process of bringing oil analysis onsite. After reviewing these elements, the end-user may find it makes sense to shift all the current routine oil analysis tests in-house or use a hybrid approach and only do some testing onsite while maintaining a relationship with a third-party lab.

Criticality Profile

The first step in this process is to build a list or scope of machinery to test and monitor. Several standard methods have been published to help the end user with this review. The use of 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 are two of these.

Both standards are based upon the concepts of Failure Modes Effects and Criticality Approach (FMECA) and allow the end-user to systematically enter into a process to select the most critical components to sample and trend. This approach is useful to help zero in on a self-run monitoring program. Some may see this as an advanced approach.

Other strategies are also available to build a strong program. A helpful starting point is to review past work history to determine what equipment failed and why the failures occurred. The cost burden from the equipment failure to the facility or process should be considered.

Some simple questions should always be in focus when doing this review, such as: Were the failures linked to the lubricant, machine design, or maintenance practice?

Also remember to review the oil testing that could be done and then determine how those tests could have helped manage the cause of the failure or failure mode.

Whichever path is used to define the program’s scope, the reliability professional should keep in mind that the main goals of developing machinery criticality profiles include safety risk, uptime, productivity, and repair expense.

Sampling Frequency

A common mistake when building a lubricant condition monitoring program is taking samples too often or, more commonly, not enough. There are several ways to make this determination.

One approach is to consult OEM documentation. These may come from OEM technical bulletins or manuals or relate to warranty recommendations for the component. The oil manufacturer may provide additional perspective for their products.

The partnership with a third-party testing laboratory may also produce further insight. It’s common for testing programs to begin with a third-party testing service, so sampling intervals may have been previously established.

ASTM standards D4378 Standard Practice for In-Service Monitoring of Mineral Turbine Oils for Steam, Gas, and Combined Cycle Turbines and ASTM D6224 also provide sample interval recommendations. ASTM D7874 may be used to refine any sampling interval obtained from the named strategies, as the interval would then be based on the FMEA analysis.

Developing a Sample Test Slate and Alarm Limits

There are several books and references available that can help with general guidelines for developing a sample test slate for compressors, turbines, engines, hydraulic systems, etc. ASTM D6224 is one such example, as it provides general guidance for broad classes of machinery. AMETEK Spectro Scientific’s TruVu360 software is another source that can also help with this process.

With 31 different component types available, the user can select from a series of components programmed into the software, such as steam turbine, gas turbine, hydraulic unit, etc., and receive guidance from the software as to which tests should be run and the recommended alarm limits and diagnostics associated with the component.

Designing Your Optimal Onsite Lab

When designing an onsite lab, there are several things to consider:

• Budget
• Space available
• Chemicals needed (if any) and sample disposal volumes (if any)
• Manpower available
• Training requirements
• Turnaround time requirements
• Data needed and the organization’s Key Performance Indicators (KPIs)

“Data needed” should be considered when building the onsite lab; however, budget, space, and manpower often drive this decision. These things can become limitations or opportunities to flourish but should be considered strongly before bringing a program onsite.

One big “win” or catch of a catastrophic failure could easily allow a budget to increase year-to-year.

It’s essential to trend the “wins” within the program as its budget can quickly change with justification. Training is much more important than many believe, as data accuracy directly impacts the quality of the analysis and repair recommendations. Of course, knowing what the data means and represents can easily make or break a program.

Lubricant Storage

New lubricant testing and storage effectiveness should be part of all lubricant testing programs. Having an onsite laboratory increases the likelihood that new lubricant testing is part of the monitoring strategy, as it isn’t intuitive to many why new oil needs to be tested. Proper lubricant storage and dispensing should be an easy win and is more important than many realize.

New lubricant testing is the first line of defense to ensure the correct lubricant is used and that clean lubricants are going into service. If new lubricant testing is not part of the condition monitoring program, stop and get this in order. There is no point in placing oil into service that is not correctly stored or managed.

Plenty of evidence in open literature shows that one of the leading causes of bearing failures occurs because of dirty oil; therefore, the lack of a new oil testing practice has a direct relationship to the overall maintenance cost. The lube room should have smart inventory practices to maximize the life of the oil, properly filter and test new oil and optimize temperature control.

Training

On-the-job training has merit when highly knowledgeable reliability professionals are available to help new personnel gain fundamental program knowledge. Poor recommendations can ensue when basic testing or data interpretation knowledge is unavailable. Whether someone is an engineer, mechanic, lab tech, chemist, etc., the chances that they have received any oil analysis-specific training earlier in their formalized education is very slim.

It is highly suggested that all individuals responsible for making maintenance decisions based on lubricant analysis data receive formalized training which should be certified through an independent body or society. Several organizations can provide the proper training to work in an oil analysis lab and understand the data generated from the lab equipment.

The International Council for Machinery Lubrication (ICML) and Society for Tribologists and Lubrication Engineers (STLE) offer oil analysis courses that help achieve these goals. Most end-users will find ICML’s MLA I class or STLE’s OMA I class are optimal for their onsite needs.

To get a positive return on investment, an organization will need the initial outlay for onsite lab equipment to pay off. Understanding what the test data means is vital, as that knowledge can be applied to increase machinery uptime and predict developing component failures.

Software

Currently, software overload can be a real thing. Consider the organization’s needs before searching for a Maintenance Monitoring System. Consider things like organizational KPIs, work order needs, and data storage requirements from different sources of the program (i.e., vibration and lube data in one place).

The software selection must help the organization easily track and highlight the “wins” within the condition monitoring program.

Here are a few simple questions to bring it all together and sustain your program. The site’s lubrication champion should drive these questions:

1. What plant equipment is critical to our process?
2. How often do we need to sample to be able to find problems?
3. What lab test(s) will give us the most useful data (i.e., ROI)?
4. Is it practical to run an in-house testing program?
5. Is our lube room storage practice improving our oil integrity?
6. What training does my team need, and how often?
7. Which software can track data, work orders, and KPIs effectively?

Working through these questions collectively with the maintenance team and aligning with the organization’s overall goals can ensure the long-standing success of the onsite oil analysis program. For more information about setting up your onsite oil analysis program, visit AMETEK Spectro Scientific’s website https://www.spectrosci.com/ or contact Lisa Williams at lisa.williams@ametek.com.

References and Acknowledgements:

https://www.astm.org/d6224-16.html

https://www.astm.org/d7874-13r22.html

https://www.astm.org/d4378-20.html

https://www.icmlonline.com/exams/Default.aspx?p=MLA1

OMA – Certified Oil Monitoring Analyst I – STLE

Special thanks to Bryan Johnson for his thoughts and insights in developing this article.

The foundation of a lubricant condition monitoring program is the ability to obtain accurate and informative data that can be related to how the machine and lubricant may fail.

In the past ten years, many industrial advances have significantly impacted the lubricant condition monitoring industry. These advances affect instrumentation, lubricant formulation, data analytics & delivery, software, and asset management.

End-users have many options to obtain high-quality test data to support their lubricant condition monitoring programs. Some may outsource samples to a third-party lab, while others may perform the sample testing on-site.

Whichever path is chosen, the most important concepts that must be followed lie in making proper measurements and analyzing the data to make critical and effective maintenance decisions.

The D02 CS96 subcommittee manages these advancements in in-service lubricant condition monitoring within ASTM International, a prominent global standards organization. The subcommittee’s primary responsibility is to promote knowledge and innovation to ensure the appropriate tests are performed, and data is correctly implemented at the end-user level.

Since 1999, ASTM’s CS96 Subcommittee has been working to create standards needed for monitoring in-service oils. The subcommittee comprises representatives from commercial laboratories, instrument manufacturers, oil manufacturers, and industrial plant end-users of test measurements.

This group of key industry stakeholders has a broad charter covering essential condition monitoring aspects.

CS96 now has over 40 standards within its jurisdiction, and they have made significant advancements in guiding and standardizing this industry in all aspects of lubricant condition monitoring.

A wide range of standards was developed to help the end-user choose the right tests for the right application, broaden testing capabilities (outside of basic oil testing), grease analysis, and how to perform statistical analysis effectively. Some highlights in the development of standards within the CS96 jurisdiction over the past twenty years include, but not limited to:

• D7720-21 Standard Guide for Statistically Evaluating Measurand Alarm Limits when Using Oil Analysis to Monitor Equipment and Oil for Fitness and Contamination
• D7690 Standard Practice for Microscopic Characterization of Particles from In-Service Lubricants by Analytical Ferrography
• D7973-19 Standard Guide for Monitoring Failure Mode Progression in Plain Bearings
• D7874-13 (2022) Standard Guide for Applying Failure Mode and Effect Analysis (FMEA) to In-Service Lubricant Testing
• D7918-17a Standard Test Method for Measurement of Flow Properties and Evaluation of Wear, Contaminants, and Oxidative Properties of Lubricating Grease by Die Extrusion Method and Preparation.
• D8185-18 Standard Guide for In-Service Lubricant Viscosity Measurement

Since the successful publication of the viscosity guide (ASTM D8185), CS96 has looked for opportunities in other areas of condition monitoring to apply this same testing-guide strategy. As a result, this same concept is currently being used with the development of a diesel engine guide. This document should be a valuable reference for mandatory diesel engine testing.

End-users are invited to be part of the group writing this new standard.

The most recent publication from the subcommittee was a joint effort between D02 Subcommittee CS96 and C (Turbine Oils). The co-sponsored STP document was published in 2022 and is available for purchase:

STP1634: Standard Guides and Practices that Support the Lubricant Condition Monitoring Industry.

The STP contains 27 papers from industry experts within the CS96 and Sub C groups. The papers showcase the latest condition-monitoring techniques developed in the last ten years.

ASTM D02 meets twice a year at various locations in North America to write and review standards. The meetings are open to anyone interested, and there are no qualifications to join other than an interest in the subject.

Significant personal benefit comes from in-person participation through meeting attendance. The next meeting is planned for Denver, Colorado, from June 25 through 29, 2023. Current committee work and minutes of meetings are available on the ASTM Web page.

Joining ASTM allows remote review or even editing and generation of ASTM documents. Steps to membership include joining ASTM through the ASTM International web page and selecting subcommittee CS96 within D02. Membership requires a small annual fee.

ASTM is an international organization with members from numerous countries. The CS96 committee within D02 provides the condition monitoring industry with a voice and place to improve the profession and technology. Its progress and success will largely result from those willing and able to participate.

Questions about ASTM CS96 Subcommittee may be directed to CS96 Subcommittee Chair Lisa Williams lisa.williams@ametek.com/

Acknowledgments:

Special thanks to Mindy Villalba, SGS North America, Co-Editor of STP 1634 and Chair ASTM D02 Subcommittee C Turbine Oils
Special thanks to Bryan Johnson, Palo Verde Nuclear Station, and Former Chair ASTM D02 CS96, for his ASTM mentorship

References:

https://www.machinerylubrication.com/Read/898/used-oil-analysis-standards