Application of Different Machine Learning Strategies for Current- and Vibration-based Motor Bearing Fault Detection in Induction Motors

In this paper, the application of different machine learning strategies for current- and vibration-based detection of bearing faults in squirrel-cage induction motors is studied. This study compares several feature extraction strategies such as a statistical and spectral analysis of vibration, a statistical analysis of the Hilbert's Transform envelope of vibration, an analysis of the currents deviation to a perfect sinusoid and a statistical and spectral analysis of the Park's Vector Modulus, with its performances being evaluated with the Support Vector Machine, Artificial Neural Network, Random Forests and Extreme Gradient Boosting algorithms. A comparison of results obtained using sampling frequencies of 0.8 kHz, 1 kHz, 2 kHz, 5 kHz and 10 kHz and analysis periods between 20 ms and 100 ms is made and promising models are achieved even with the lowest sampling frequencies.