Abstract
The paper discusses the possibility of detecting damage to the bearing of an asynchronous motor operating as part of a variable-frequency drive using machine learning algorithms and the use of multi-band filters during processing information about the stator current. Detecting a fault in an asynchronous motor when powered by an inverter is more difficult than when powered directly from the network due to the masking of a fault signs by higher harmonics from the inverter in the stator current. The effectiveness of using multiband filters (in particular, a type II Chebyshev filter) to solve the problem of extracting fault symptoms is shown. The work used an approach based on a type II Chebyshev filter, which has a flat amplitude-frequency characteristic in the passband, which is important for preserving the signal amplitude and, in comparison with the Butterworth filter, which also has a flat amplitude-frequency characteristic in the passband, has a faster decline in the suppression band. Multi-band filtering was used at the stage of extracting fault features from stator current signals and then feeding them into machine learning classifiers. To recognize the fault, after using multi-band filtering, four machine learning methods were applied, namely: artificial neural network, K-nearest neighbors, support vector machine and naive Bayes classifier. All methods showed a very high probability of correct fault recognition. The highest efficiency (more than 90% probability of correct fault recognition at various operating frequencies and engine loads) was shown by classifiers based on an artificial neural network and the K-nearest neighbors method.
Keywords
bearing fault diagnosis, variable frequency drive, machine learning, induction motor, fast Fourier transform, multi-band filters, type II Chebyshev filter, current signature analysis, signal components, signal statistical features
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Ahmed O.H., Metelkov V.P., Zyuzev A.M., Esaulkova D.V. Induction Motor Bearing Fault Diagnostics in Variable Frequency Drive Based on Machine Learning Using Multi Band Filters. Elektrotekhnicheskie sistemy i kompleksy [Electrotechnical Systems and Complexes], 2024, no. 1(62), pp. 56-64. (In Russian). https://doi.org/10.18503/2311-8318-2024-1(62)-56-64