Designing a World-Class Oil Analysis Program

Anyone who has read Practicing Oil Analysis magazine on a regular basis over the past five years should be well versed on the impact oil analysis can have in helping improve equipment reliability and maintaining production uptime.

From providing a predictive early warning of impending failure, to seeking a proactive root cause solution, there can be little doubt that oil analysis is an effective condition-monitoring tool.

When this occurs, the usual reaction is to blame either the technology or the oil analysis lab for having failed to warn of impending doom. In extreme cases, the temptation may be to seek out a different lab which, rightly or wrongly, is billed as “better” than the incumbent lab.

5 Steps to Designing a World-Class Oil Analysis Program:

Step No. 1. Initial Program Setup

A program designed around this type of sound footing, requires development of an overall oil analysis strategy in conjunction with a Failure Mode and Effects Analysis (FMEA). This is often performed as part of a more comprehensive Reliability-Centered Maintenance (RCM) program.

The FMEA process looks at each critical asset, and based on component type, application and historical failures, allows test slates, sampling frequencies and targets and limits to be selected. These items will address the most likely or prevalent root cause of the failure.

Now let’s think about the maximum permissible water, which should be set as a goal-based limit from an FMEA and criticality assessment. If the plant FMEA indicates a need to keep water content below 200 ppm (0.02 percent), then the only option to trend water content over time to ensure compliance would be the Karl Fischer test, because Fourier Transform InfraRed (FTIR )is typically insensitive below 500 to 1,000 ppm.

Step No. 2. Sampling Strategy

Of all the factors involved in developing an effective program, sampling strategy has perhaps the single largest impact on success or failure. With oil analysis, the adage “garbage in, garbage out” definitely applies. While most oil analysis labs can provide advice on where and how to sample different components, the ultimate responsibility for sampling strategy must rest on the end user’s shoulders.

Take the real-life example of a reliability engineer at a plywood plant who had outright rejected oil analysis as an effective conditioning-monitoring technique.

His misguided belief was based on the notion that because the plant he worked at had experienced four hydraulic pump failures in the past two years, none of which had been picked up by oil analysis, the technology simply did not work. But is it really the technology that’s at fault?

When the same engineer was asked from where in the system he was taking the sample, he seemed genuinely shocked that anyone would sample a hydraulic system anywhere other than the reservoir.

However, by doing so, any wear debris from the failing pump would show up only in the oil sample bottle after finding its way through the system, which included valve blocks, untold numbers of actuators and a 3-micron return line filter, and into a 5,000-gallon reservoir where it would be diluted.

 

Step No. 3. Data Logging and Sample Analysis

Assuming the sampling strategy is correct and the program has been designed based on sound reliability engineering goals; it is now up to the lab to ensure the sample provides the necessary information. The first stage is to make sure the sample, and subsequent data, is logged in the correct location so trend analysis and rate-of-change limits can be applied.

 That is the lab’s responsibility, right? What if two successive samples are labeled slightly different? For example, two samples are labeled unit IDs GB-3456 and 3456. While logic might tell us that the prefix GB simply means “gearbox,” imagine the difficulty the lab faces, confronted with as many as 2,000 samples daily. While carelessness and inattentiveness on the part of the lab are inexcusable, it is incumbent on the end user to ensure the consistency of information that is logged and used for diagnostic interpretation.

f the lab is requested to run a particle count, does it perform this test first to minimize the possibility of further lab procedures contaminating the sample, or is it left until last? How often does the lab run QA samples - samples of known chemical composition inserted in the daily run to ensure test instruments are within acceptable QC limits? Does it run them every 10 samples, every 50, or not at all?

For some customers, there is a misguided belief that for a $10 oil sample, they should receive a report that indicates which widget is failing, why it is failing and how long that widget can be left in service before failure will occur. If only it were that simple!

The lab’s role is to evaluate the data so that complex chemical concepts such as acid number or the presence of dark-metallo oxides makes sense to people who may have many years of maintenance experiences, but haven’t taken a high school chemistry class in many years.

The lab cannot be expected to know - unless it is specifically informed - that a particular component has been running hot for a few months, that the process generates thrust loading on the bearings, or that a new seal was recently installed on a specific component that is now showing signs of excess water in the oil sample.

Evaluating data and making meaningful condition-based monitoring (CBM) decisions is a symbiotic process. The end user needs the lab diagnosticians’ expertise to make sense of the data, while the lab needs the in-plant expertise of the end user who is intimately familiar with each component, its functionality, and what maintenance or process changes may have occurred recently that could impact the oil analysis data.

Likewise, evaluating data in a vacuum, without other supporting technologies such as vibration analysis and thermography, can also detract from the effectiveness of the CBM process.

Step No. 5. Performance Tracking and Cost Benefit Analysis

Oil analysis is most effective when it is used to track metrics or benchmarks set forth in the planning stage. For example, the goal may be to improve the overall fluid cleanliness levels in the plant’s hydraulic press by using improved filtration. In this case, oil analysis - and specifically the particle count data - becomes a performance metric that can be used to measure compliance with the stated reliability goals.

Metrics provide accountability, not just for those directly involved with the oil analysis program, but for the whole plant, sending a clear message that lubrication and oil analysis are an important part of the plant’s strategy for achieving both maintenance and production objectives.

The final stage is to evaluate, typically on an annual basis, the effectiveness of the oil analysis program.

This includes a cost benefit evaluation of maintenance “saves” due to oil analysis. Evaluation allows for continuous improvement of the program by realigning the program with either preexisting or new reliability objectives.At a molecular level, viscosity is a result the interaction between the different molecules in a fluid. This can be also understood as friction between the molecules in the fluid.