The blotter spot test has been discussed numerous times in the pages of Machinery Lubrication magazine. It not only is one of the oldest oil analysis tests (mid-19th century) but endures as one of the most effective at detecting and even quantifying certain lubricant abnormalities.

However, the blotter spot test is not commonly known as a test for detecting and examining particles in oil such as wear debris and dirt.

As a practical matter, its ability to reveal normal and even slightly abnormal amounts of solid particles is limited, especially without the aid of a microscope. This generally is true with other applications of blotter spot testing.

In other words, the lack of a visible structure (rings, starbursts, pasty center, etc.) is an indication of the absence of the target condition. Because of this, the blotter spot test is less likely to produce a false negative compared to other more advanced analytical methods.

While each method has its own unique interferences and lower sensitivity limits, the ability of blotter spot testing to provide a reliable alert to abnormal particle concentrations is undisputable.

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Sampling is arguably the most important step in the oil analysis process. If the sample in the bottle fails to effectively represent the lubricant and the condition of the machine from which it was drawn, no meaningful conclusions can be from the oil analysis data. This article presents a comprehensive methodology for designing the sampling system and for obtaining a sample from equipment commonly found in the industrial or fleet environment. Useful hints and tips are provided to ensure that oil analysis is on target to meet the expectations of the condition monitoring organizations in pursuit of optimized asset reliability.

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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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The best maintenance techniques for mechanical machinery are condition-based techniques. Condition-based maintenance is maintenance prescribed by the real-time needs of the machine according to changes in specific operation conditions. These conditions fall into two categories.

The first set of conditions are those that present a risk to a machine’s health if allowed to persist. These are operating and environmental conditions that precede failure, i.e., root causes of failure. They are not failure symptoms, which is after the fact. Examples of root cause conditions are misalignment, lubricant contamination, and overheating. The activity of detecting and correcting root cause conditions is referred to as proactive maintenance. Its singular purpose is to extend a machine’s operating life.

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When the history of the condition monitoring field is written, there will likely be a chapter, or at least a few pages, on the odd paradox surrounding how infrequently use of used filter testing was employed (in the beginning). The assumption is that filter testing will eventually enjoy widespread use as misconceptions and ignorance of its benefits fade away. As I write today, the vast majority of people engaged in condition monitoring are oblivious to the wealth of information found in used filters.

The target application relates primarily to critical equipment, i.e., those that are expensive to repair or have high downtime costs. A well-engineered condition monitoring program should align the data being gathered with the failure modes we seek to detect. Granted, these are failure modes of highest risk to the machine, based probably on occurrence and consequences (business interruption, etc.).

Think of the logic behind this. Filters are intended to purify by removing solid contaminants that are harmful to the oil and the machine. If the filter is doing a good job, it is removing contaminants as fast as they are entering (ingression). This is known as mass balance. The number of particles coming into the oil system (particle ingression) equals the number being caught by the filter (particle removal); this achieves a stabilized and controlled oil cleanliness level. While particle removal is advantageous to the oil and the machine, it gets very low marks when the oil being cleaned is sampled for analysis.

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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.

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The term “oil analysis” has been used with respect to lubrication and machine reliability since the 19th century. Yet, over the years I have seen only a few attempts to assign a specific definition to the activity despite its widespread application in industry. Most of these definitions are too narrow or fail to include important integral activities such as machine wear monitoring and fault detection.

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Particles. Think about it. Tribological studies on wear-related machine failure rank particle-induced abrasion as the No. 1 cause of wear. This is often referred to as three-body abrasion, where particles become interposed between two surfaces in relative motion, leading to damage (cutting) of both surfaces. Particles contribute to an assortment of other types of wear and failure too, including surface fatigue, silting and erosion.

With this in mind, it is only natural that monitoring the size and concentration of particles in lubricating oil would be of keen interest to the astute reliability professional. It’s no wonder the particle counter is a leading oil analysis instrument and is high on the list of favored instruments in the condition-monitoring field in general.

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Oil analysis is about surfacing problems that were otherwise hidden from view. We’ve all heard the phrase “if it ain’t broke don’t fix it,” but an important corollary is “if it is broke, fix it fast.” The basic problem with this strategy is not knowing when something is actually broken.

For many organizations, oil analysis offers an effective solution, but it works only if users are literate in its language and apply it effectively. While oil analysis is not a panacea for all machine reliability problems, it does offer many special opportunities. For one, the oil is typically the carrier of both the root cause and symptoms of impending failures.

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