Bearing Fault Detection and Recognition from Supply Currents with Decision Trees

This paper considers the tasks of detecting and recognizing bearing faults in electric motors from the signals collected from supply currents, using machine learning techniques. In particular, following recent trends in AI, the main point of interest was focused towards interpretable solutions that provide explanations on the decisions taken by the classifiers. For this reason, decision trees were chosen, since they represent a classic machine learning approach which inductively learns tree structures from a collection of observations. Paths along the learnt trees can be easily interpreted as plain classification rules. An extensive experimental comparison shows the strong generalization capabilities of such a classifier. In particular, the present work reports results obtained in a highly challenging scenario, usually overlooked in the literature, where the system is tested on configurations of radial and torsional loads that have not been observed during training. The approach achieves over 90% of accuracy even on this cross-load generalization setting.