Evaluation of Machine Learning for Sensorless Detection and Classification of Faults in Electromechanical Drive Systems

Abstract

Abstract Obtaining new information and creating value from present measurements without introducing additional sensors is cost-efficient and mitigates data that is collected and stored by information systems but not used. In electromechanical drive systems, defect states of synchronous motors can be detected based on measurements of the motor current. While there is a tendency to (exclusively) apply Deep Learning models to such problems, we argue that, for appropriate problem settings, alternatives should also be evaluated and at least be used as benchmarks. This paper addresses the question of whether non-Deep Learning methods are competitive to Deep Learning ones for sensorless detection and classification of faults in electromechanical drive systems. For this multi-class classification problem, a systematic evaluation of selected traditional, ensemble, and Deep Learning classifiers is conducted for a data set with one normal state and ten fault states of an electromechanical drive system. In addition to working on the raw input data, the impact of Recursive Feature Elimination is compared to dimensionality reduction with Principal Component Analysis. Accuracy, computational complexity, and engineering effort of the different Machine Learning pipelines are compared. A key finding is that the appropriate combination of feature elimination and Machine Learning model yields high accuracies while allowing to massively reduce the number of features, hence making the detection of fault states less computationally expensive.