Cross-Load Generalization of Bearing Fault Recognition with Decision Trees.

The literature on condition monitoring is nowadays characterized by a wide variety of machine learning approaches. We argue that, in most of the works, the experimental evaluation is conducted in an oversimplified scenario, where training and test data contain samples obtained under the same radial and torsional load conditions. In this paper, we propose to apply an interpretable machine learning model, namely decision trees, to perform fault detection and recognition across different load configurations, a challenging benchmark that requires general-ization capabilities. The rules extracted from the trees provide explanations of the classification process.