Prediction of Component Level Degradation in a Hydraulic Rig using Machine Learning Methods

Predictive maintenance is one of the main trends noted in Industry 4.0, the ongoing era of automation and digitization in the manufacturing sector. Condition monitoring, a widely prevalent technique in Predictive Maintenance involves constant monitoring of systems through sensors and technologies enabling timely intervention to prevent sudden/unplanned breakdown that affects production, man-hours, inventory, and in worst cases, safety. This paper uses a data-driven approach to identify and classify faults in a multi-component hydraulic rig. Different Feature extraction/selection methods from the historical data with multiple sensor readings of different sampling frequencies (asynchronous data) were explored and compared. Supervised Learning models were built on these features to distinguish and detect the different levels of components' degradation. In addition, given the challenge of lack of annotated data in Industrial setups, unsupervised Clustering and anomaly detection algorithms were also examined to detect faults in the system.