Most often, users associate an oil analysis program with a systematic early alert to oil or machine failure, i.e., damage control. While these benefits are helpful and frequently achieved, they should be regarded as low on the scale of importance compared to the more rewarding objective of failure avoidance.

Whenever a proactive maintenance strategy is applied, three steps are necessary to insure that its benefits are achieved. Since proactive maintenance, by involves continuous monitoring and controlling of machine failure root cam:es, the first step is simply to set a target, or standard, associated with each root cause.

In oil analysis, root causes of greatest importance relate to fluid contamination (particles, moisture, heat, coolant, etc.) and additive degradation. However, the process of defining precise and challenging targets (e.g., high cleanliness) is only the first step. Control of the fluid’s conditions within these targets must then be achieved and sustained. This is the second step and often includes an audit of how fluids become contaminated and then systematically eliminating these entry points. Often better filtration and the use of separators are required.

The third step is the vital action element of providing the feedback loop of an oil analysis program. When exceptions occur (e.g., over target results) remedial actions can then be immediately commissioned. Using the proactive maintenance strategy, contamination control becomes a disciplined activity of monitoring and controlling high fluid cleanliness, not a crude activity of trending dirt levels.

Finally, when the life extension benefits of proactive maintenance are flanked by the early warning benefits of predictive maintenance, a comprehensive condition-based maintenance program results. While proactive maintenance stresses root cause control, predictive maintenance targets the detection of incipient failure of both the fluid’s properties and machine components like bearings and gears. Following the oil sampling procedures, selection of appropriate sample testing procedures, and interpretation of test results outlined in this section, immediate corrective action can then be directed to effectively avoid failure chain reactions and further self destruction.

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Many have read the well-documented case studies that convincingly demonstrate the practice of used oil analysis as a sound approach to reduce maintenance and downtime costs. However, for most users, these rewards have evaded their best efforts due to common implementation errors. Like many pursuits in life, there is often a very fine line that marks the division between success and failure. Success in the analysis of lubricating oils seems to consist of a series of such fine lines that must be carefully navigated.

This booklet draws on many years of experience in working with successful users. Its goal is to define a well-marked pathway to insure the success of new users, while at the same time, help existing users out of the slippery pitfalls they may have encountered. This will be accomplished by first identifying the ten most common reasons why oil analysis programs fail and then transitioning them into durable strategies that effectively overcome them.

It will be shown that these strategies depend much more on excellence in execution than the sophistication of underlying technologies. The guiding principle is the condition-based maintenance philosophy due to its penetrating sensitivity to both the causes and effects of failure. The familiar approaches of proactive maintenance (failure root-cause monitoring) and predictive maintenance (failure symptom monitoring) fall under the broad category of condition-based maintenance.

The practical wisdom of oil analysis pundits worldwide teach us that the most successful programs are those that are thoughtfully designed after careful need evaluation with mission and goals well defined. The emphasis is on designing quality and excellence in the beginning, not force-fitting it in along the way (see Figure 1 ). The many strategies and subsidiary tactics described herein are designed to help users achieve this as efficiently and effectively as possible.

The post The Ten Most Common Reasons Why Oil Analysis Programs Fail & the Strategies That Effectively Overcome Them (booklet) appeared first on Tesibis.