Think of oil analysis as being like the television game show Wheel of Fortune. There’s a message to be told but you’ll only see and understand it if you expose enough letters in their proper order. Some of these letters are in the oil but many are elsewhere. These include current knowledge of machine operating environment, service history, inspection reports and condition monitoring data from companion technologies. Start by carefully listing what questions you want oil analysis to answer. Then work backwards to determine test slate required to answer these questions. Optimize the data set, don’t minimize or maximize.

Many labs are far better at analyzing the oil than data presentation and interpretation. Expertise in analytical chemistry does not always translate to effectiveness in machine condition monitoring. In fact, frequently users are better served by engaging a lab to provide timely and accurate data than over reliance on interpretation and reporting services. With the right tools they can easily customize and manage their own data using Web-based products offered by most commercial labs or with proprietary software sold independently.

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Most oil samples are taken based on a fixed schedule. For large, stationary rotating equipment, monthly or bi-monthly samples are common. Proactive maintenance programs depend on regular checks for oil cleanliness, dryness and lubricant quality.

However, machines can and do fail for a variety of reasons, and there is a certain randomness to the onset of these failures. Furthermore, the failure development period is equally unpredictable, with some failures taking months to develop, while others are sudden and abrupt.

In the March-April 2013 issue of Machinery Lubrication, I addressed machine criticality analysis as an essential tool to define the Optimum Reference State (ORS) for numerous lubrication and oil analysis activities. The Overall Criticality Matrix (OCM) is constructed from two assessments: the Machine Criticality Factor (MCF) and the Failure Occurrence Factor (FOF).

The MCF relates to the consequences of machine failure while the FOF relates to the probability of machine failure. Both the MCF and the FOF are highly influenced by the effectiveness of “early fault detection.” In other words, the effectiveness of early fault detection sharply reduces machine criticality (for details on this, read the article at www.machinerylubrication.com/Read/29346/machinery-criticality-analysis).

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