8. Measure Quality by Oil Analysis

Designing a World-Class Oil Analysis Program 

The impact oil analysis can have in helping improve equipment reliability and maintaining production uptime is huge. 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. 

What exactly is involved in designing an oil analysis program that provides maximum payback?

Five Steps to designing a World-Class Oil Analysis Program

Developing an effective oil analysis program requires careful planning. The program should be developed with a careful game plan in place based on a stated series of reliability goals. There are five basic steps to developing an oil analysis program:

Oil analysis in 5 steps

To maximise the opportunities for success, these steps should be performed in this order so that the program is developed on a sound footing.

Step No. 1. Initial Program Setup 

The overall structure and foundation of an oil analysis program should be based on sound reliability engineering goals. These goals should guide the end user through the process of designing and implementing the program.


While the lab’s experience in developing effective oil analysis programs can be used to support the design process, it is ultimately the end user’s responsibility to ensure the program meets the company’s goals and reliability objectives. In particular, attention should be paid to the types of test procedures used by the lab under different circumstances. 

Program design - including test slate and procedure selection - is dependent on end user defined goals. 

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.

While bottom sampling can be useful in determining the presence of unusual levels of water, sludge and other debris, it is unlikely to yield any meaningful data from an oil analysis lab. Of course, sample strategy involves more than just sampling location. Sampling method and procedure, bottle cleanliness and hardware all factor into the sampling equation. 

Perhaps second only to location in importance, is the provision of collateral information when the sample is submitted to the lab. For industrial equipment, as few as one sample out of 10 is submitted to the lab with appropriate information about oil type, hours on the oil, filter changes or the addition of make-up oil. Without suitable information, oil condition parameters such as viscosity or acid number cannot be compared to the new oil and trend analysis cannot be performed effectively. 

Without exception, it is the responsibility of the end user to ensure that any and all pertinent information that can be used by the lab in the analysis and interpretation of the data be sent to the lab with each and every sample. Failure to do so simply means that the lab is guessing at whether or not any of the data is significant and should be flagged for attention. 

Step No. 3. Data Logging and Sample 


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.

Once the sample has been properly set up at the lab, the actual sample analysis is next. 

This is an area where end users are definitely at the mercy of the lab and its quality assurance (QA) and quality control (QC) procedures.   

Data logging and sampling flowchart

5 steps to a world class oil analysis programme

Step No. 4. Data Diagnosis and Prognosis 

Diagnostic and prognostic interpretation of the data is perhaps the step where the most antagonistic relationship can develop between the lab and its customers. 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-metallic 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.

While the end user must bear some responsibility for correctly evaluating the data, the lab does have some culpability. 

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. 

Fanpro - oil analysis


There can be little doubt that oil analysis is an integral part of any condition-based maintenance program. When used effectively, it can warn of impending failure, direct us to the root cause of a problem, or point to areas of opportunity we perhaps didn’t know existed. However, just like you wouldn’t buy a used car without checking under the hood, taking it for a test drive and kicking the tires, don’t merely assume that filling the sample bottle with oil and sending it to the lab will produce the desired results. 

Source: Noria

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