World-class condition monitoring of hydraulic systems involves the successful integration of a number of strategic elements. While in the past, walk-around inspections and gage data were the primary means of monitoring system health, today’s modem oil analysis programs apply a host of sophisticated new tools and instruments. Reliability teams at the plant site frequently commission small laboratories. In many cases the instrumentation suite includes portable and unattended sensors. The situational context is changing too as today’s hydraulic systems are increasingly designed for higher pressures, speeds, and temperatures. This paper presents a review of strategic elements that, when well conceived and implemented, can deliver vital aiding information for achieving even the toughest condition-based maintenance goals. These include the selection of test slate, deployment of incipient failure advisories, setting of targets and limits that define nonconforming conditions, exception testing, and proactive maintenance.

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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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Abstract: In oil analysis, well placed alarms and limits are like trip wires, alerting operators and technicians to an untoward or threatening condition. Oil analysis limits can vary considerably according to machine type, oil type, and reliability goals. This paper discusses four distinct types of limits and how they are applied to different machine and lubricant applications: goal-based limits, aging limits, rate-of-change limits, and statistical limits.

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Optical particle counters (OPC’s) have a long history of use in industrial hydraulic applications. Traditionally, their success has been limited to scientific laboratories and other highly controlled environments. However, in recent years, attempts have been made to apply the use of OPC’s to the particle counting of used hydraulic fluids and industrial lubricants. As a result, serious concerns have been raised regarding the accuracy and reliability of OPC’s in such applications. The objective of this bulletin is to present important facts from reliable and documented sources for the general benefit of existing or prospective users of OPC’s. As particle counting moves into the mainstream of machine condition monitoring, users must have reliable information to identify and select appropriate technologies.

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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.

The post Tricks to Classifying Wear Metals and Other Used Oil Suspensions appeared first on Tesibis.

I recently analyzed a database of more than 30,000 oil analysis reports from diesel engine samples (Class 8, long-haul trucks). With so much information at my fingertips, I was like a kid in a candy store.

Today’s database software products empower users with versatile analytical tools that can rapidly search, sort, graph and statistically analyze data. With the right queries, one can turn huge amounts of raw oil analysis data into new interpretation guidelines, fault trees and rule sets for the oil analyst.

Naturally, after a couple of hours of slicing and dicing this rich database, I discovered many interesting facts. One such fact relates to copper. In diesels, copper is second only to iron as the most abundant wear metal.

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The monitoring of particle contamination in lubricating fluids is a critical indicator of incipient and impending failures. Recent research, under both laboratory and field conditions of the contaminant sensitivity of bearings, reveals a well defined cause and effect relationship between contaminant levels and relative machine life.

This paper discusses the contaminant sensitivity of bearings as well as turbines, diesel engines, gear systems, and hydraulic systems. Also discussed is the benefit of applying contaminant monitoring to both proactive and predictive maintenance programs. Proactive maintenance is the process of monitoring root causes (pre-degradation conditions) as opposed to predictive maintenance which focuses on impending failure conditions.

The post Particle Contamination — Both a Cause and a Result of Mechanical Failure appeared first on Tesibis.

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.

Imagine the filter inside your machine is made of fibers the size of telephone poles, stacked randomly in all directions, many layers thick. Each juncture where poles touch is a drop of super glue for support. To emulate actual operating conditions, the stack of poles is placed on a large moving and vibrating table.

Now, imagine that the contaminants inside your oil are lumps of gelatin, clumps of tar, ping-pong balls, marbles, tree branches, powdery sand, beanbags, strips of sheet metal, streams of honey, wet rags and beach balls. To begin our example, suppose that you had large containers of these different contaminants beside you as you perch on top of scaffolding hovering above the stack of telephone poles.

The post How Filters Work to Control Contamination in Oil appeared first on Tesibis.

I realize that this statement – No Contamination, No Wear, No Kidding –  may not sit well with some readers, such as a few purists within the tribology community. But before anyone gets too excited, let me start by listing my assumptions:

1. The machine is well designed, manufactured and installed.
2. It is used in the target application within rated operating parameters (loads, speeds, etc.).
3. The lubricant is properly selected and produces an oil film (EHL or HDL).
4. Lubricant levels and change intervals are properly maintained.
5. Oil analysis and other condition monitoring practices are performed to best practice.

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