Machine Learning (ML) making Predictive Maintenance actually "predictive"

Unplanned down times and shut-downs, one of the major challenges industries face when disruptions happen due to an unanticipated break-down event. So plants always rely on preventive maintenance to avoid such occurrences. However , in order to optimize the productivity it is always desirable to have minimal number maintenance schedules. Data driven "predictive analytics" is one of the better options to optimize between maintenance stoppages and risk of unplanned disruptions.

AI/ML can be really handy in developing Predictive Maintenance models. Recurrent Neural Network (RNN) models like "Long short-term memory (LSTM)" can be used to develop accurate Predictive models for Maintenance. Historical past data of failures can be used to develop failure models and to estimate remaining useful life (RUL). Such models use the time variation of sensor data to identify possibility and timeline of failure. This way shutdowns can be planned properly minimizing the impact / cost.