When working from a single sample, it is common for labs to classify wear particles according to standardized shapes such as platelets, chunks, ribbons and spheres. The task of deriving meaning from the number and size of particles in the different classifications is much more difficult. Condition monitoring is not about science – it’s about understanding and reporting what is happening, why it’s happening, where it’s happening, and how severe or threatening the condition might be. This can be a daunting task, to say the least, especially if you are not being assisted by a particle-counting technology.

The lubricant co-exists with the machine and has an active presence in its critical frictional zones. As such, the progression of wear-related machine failures does not go unnoticed by the lubricant. The byproducts of wear and surface damage become suspended in the lubricant, embedded in the filter, or stratified as sediment in nooks and crannies.

As failure advances, most wear modes produce more particles, and some also produce larger particles. In certain cases, what was thought to be an advanced failure state may suddenly appear benign or in decline. There are reasons for this, so do not be fooled. The wounds and excavations from wear do not heal over on their own.

The time has come to increase the specificity of wear particle characterization. The four basic shapes were a good start, but there is much more we can learn and apply. For those who understand vibration, imagine being limited to vibration overalls or only what is produced in the low-frequency velocity spectrum. Likewise, thermal imaging has shown us how to look far beyond discrete temperature values or trends. This analogy applies to wear debris analysis as well. The appearance of particles holds many clues that generally go unnoticed or are just not understood.

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The most common methods for initial detection of abnormal levels of wear debris in used oils include elemental analysis, ferrous density analysis (DR, etc.), particle counting and patch testing. For some users, because of the criticality of the application, all of these screening tests for wear metals are integrated into the routine test slate.

In such cases, when sampling is done correctly, it would be rare for the abnormal production of wear metals to go undetected. However, when only one or two of these methods are routinely deployed, there is a distinct risk that an incipient (early stage) failure condition may be missed.

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The analysis of power train lubricants for the purpose of detecting faults and abnormal wear patterns is a well developed practiced in mobile equipment applications. However, these same techniques don’t always transfer successfully into stationary equipment applications for many users. In recent years new approaches and techniques have been perfected to substantially improve the detection of incipient and developing faults in bearings and gear units using wear debris analysis. The approach is more systemic as opposed to the application of any singular new or emerging technology. It begins with improvements in the sampling process to enrich the data and proceeds through the use of tactics that strengthen the signal-to-noise ratio. After detection is confirmed, the final analytical phase involves wear particle identification using both classic and advanced techniques. Key Words: wear debris analysis, spectroscopy, wear particles, ferrography, fault detection, predictive maintenance, tribology, wear particle identification, oil analysis, condition monitoring, elemental analysis, ferrous density, microscopic analysis, ferrometrics.

The post Best Practices in Maximizing Fault Detection in Rotating Equipment Using Wear Debris Analysis appeared first on Tesibis.

The analysis of power train lubricants for the purpose of detecting faults and abnormal wear patterns is a well developed practice in mobile equipment applications. However, these same techniques don’t always transfer successfully into stationary equipment applications for many users.

In recent years new approaches and techniques have been advanced to substantially improve the detection of incipient and developing faults in bearings and gear units using wear debris analysis. The approach is more systemic as opposed to the application of any singular new or emerging technology.

It begins with improvements in the sampling process to enrich the data and proceeds through the use of specific strategies and tactics. After detection is confirmed, the final analytical phase involves wear particle identification using both classic and advanced techniques.

The post Maximizing Fault Detection in Rotating Equipment Using Wear Debris Analysis appeared first on Tesibis.

Spherical particles have been found in microscopic analysis in many different machines. The phenomenon of perfectly round spheres has been a topic of interest since their discovery and a number of theories have been formulated for their origin.

While more than one theory exists, their source can be narrowed down by considering certain factors such as particle size and texture, equipment type, surrounding debris, stage of equipment life and environmental conditions. Table 1 summarizes the predominant theories of the origin of spheres in oil analysis.

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The most widely used laboratory methods for initial detection of abnormal levels of wear debris in used oils include elemental analysis, ferrous density analysis (DR, etc.), particle counting and patch testing.

For some users, because of the criticality of their machines, all of these screening tests for wear metals are integrated into the routine test slate. In such cases, when sampling is done correctly, it would be rare for the abnormal production of wear metals to go undetected.

However, when only one or two of these methods are routinely deployed, there is a distinct risk that an incipient (early stage) failure condition may be overlooked or dismissed as inconsequential.

The post Tactics For Identifying Wear Metal and Solid-particle Suspensions appeared first on Tesibis.

The analysis of powertrain lubricants for the purpose of detecting faults and abnormal wear patterns is a useful practice in mobile equipment applications. Unfortunately for many users, these techniques don’t always transfer successfully into stationary equipment applications. In recent years, new approaches and techniques have been advanced to improve the detection of incipient and developing faults in bearings and gear units using wear debris analysis.

As opposed to the application of any singular new or emerging technology, these new methods are more systematic and functional. It begins with improvements in the sampling process to enrich the data and proceeds through the use of specific strategies and tactics. After detection is confirmed, the final analytical phase involves wear particle identification using both classic and advanced techniques.

Like so many endeavors, success depends more on the quality of execution than the strength of the underlying technologies. This idea can be concluded from the fact that while a great deal of new knowledge and technology has been advanced, for the vast majority of industrial organizations employing wear debris analysis, little has changed in either their tools or approach.

The post The Benefits of Using Wear Debris Analysis in Industrial Machinery appeared first on Tesibis.

I often mention the wear debris universe when I lecture on oil analysis topics. This refers to the extensive array of wear particle technologies and tactics that can help reveal the true tribological condition of a machine.

Individually, these tools are often inconclusive when it comes to identifying the source, cause and severity of abnormal wear conditions. They may not even be able to identify the problem at all. Yet when used in combination, they can expose a vivid image of a current or impending failure condition.

A skilled analyst should be well aware of the strengths and weaknesses of these technologies and tactics. Not all of these tools need to be at your fingertips, but nonetheless should be available when called upon. Too often, an inexperienced technician will attempt to draw a premature conclusion from little more than a sliver of information in the wear debris universe.

Examples might be a muted iron trend from elemental data or stabilized ISO codes from particle counting. Used alone, these technologies might telegraph to the diagnostician the erroneous appearance of machine health (or disease). Unseen may be an incipient but serious wear condition in need of attention.

Common front-line technologies used for screening purposes include ferrous density analysis, elemental spectroscopy, particle counting and patch testing. Collectively, these technologies pack a powerful punch and are credited with scores of predictive maintenance “saves”.

However, in a high percentage of cases, these technologies would not have earned their hero status if it weren’t for the other tests and methods that peered deeper into the core of the problem.

These include secondary sampling points, filter debris inspection, magnetic plug analysis, sump sediment analysis, SEM-EDS, XRF, ferrography (all methods), acid-dissolution spectroscopy, particle heat treatment, particle impaction testing, chemical microscopy, digital shape profiling, percent of large ferrous particles, rotrode filter spectroscopy, TGA, gravimetric analysis, ultracentrifuge (separation of soluble metal fraction), pore blockage particle counting … and the list goes on.

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