Charles - The Oil Analysis AI

To understand how all this applies to equipment maintenance, start with what’s commonly called big data. Accessing potentially vital information buried in the reams of big data is as vital to maintenance as it is to economics and investments. For equipment, such as engines, gears and hydraulics, AI has become the platform to enable identification of troubling trends in a sample. It accesses thousands of data points, many of which are inaccessible through traditional analysis, and produces a report for the analyst to review.

Machine learning is a subset of AI. Here, the focus is on patterns and relationships between data. The machine learns from historical material fed by the data analyst. Information of this type is developed into a model that enables the computer to learn. The model, unlike traditional analysis of oil samples, is not rules-based, allowing for different interpretations to be easily factored in.

There is a direct relationship between the quality of traditional oil analysis and the machine learning model. Quality demonstrated by the experience of the analyst in many ways is as fundamental to machine learning as it is to the AI platform. Both learn from experience in much the same way as an apprentice learns a craft from a longtime practitioner.

The reality is that the machine continues to learn after AI does the data points’ heavy lifting prior to the analyst’s review. The impact of this process on maintenance is clear. Unlike traditional analysis and its after-the-fact detection of abnormal trends, an AI platform-driven oil analysis identifies precursors of wear on equipment or changes in fluid conditions. A department can take action before there is either downtime or a reduction in the equipment’s useful life. One example already in use by an international certification agency is a platform containing an aggregate consisting of millions of samples and results from more than a decade of analysis.

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