Use Machine Learning To Predict Remaining Useful Life

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Machine learning (ML) and stream processing technology are powerful solutions for remaining useful life (RUL) analysis. Manufacturers can use the large amounts of data produced by sensors combined with human inspections of finished pieces to train ML algorithms. The ML tools can then proactively alert operators when a tool is approaching its end of life, allowing them to schedule the replacement for a convenient time. Stream processing algorithms can also process all the sensor data being generated by any number of production machines, make on-the-fly comparisons with historical data, and increase the accuracy of ML algorithms.

This solutions flyer explains how smart selection and application of ML tools combined with the availability of clean, governed datasets help manufacturers optimize their maintenance and wear part replacement schedules.

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