Intelligent forecasting of oil changes with onsite oil analysis leads to maintenance cost savings

Organizations with good lubrication management programs have oil analysis programs reporting 80% of samples to be in normal status condition. With a normal status, most maintainers continue to change oil based on a time interval, typically OEM guided.  Improved predictive data analytics of oil analysis generated with onsite instrumentation can provide valuable forecasting information such as when the optimum oil change point occurs. Other information on optimum sampling interval to ensure effective monitoring may be calculated also.

This presentation outlines a current capability available to our onsite oil analysis customers, the application of machine learning algorithms to evaluate oil properties, predict degradation patterns, and estimate the useful life based on current status.  We present examples from large engine owners, where the forecasting techniques can lead to significant cost savings with this capability, thus highlight the transformative potential of predictive AI in optimizing oil management practices, ultimately extending the lifecycle of resources in the industry.