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Fault Analysis and Diagnosis for Induction Motor Based on Hilbert Transform and Support Vector Machine Classification Method

journal contribution
posted on 2024-11-17, 13:48 authored by Yangsheng Zhang, Yi Liu, Wei Xu, Md Rabiul Islam
With the wide applications of the asynchronous induction motor (IM), various kinds of the electrical and the mechanical faults have been appearing, the main ones of which are the inter-turn short circuit of the stator, the broken-bar of the rotor and the air-gap eccentricity. This paper analyzes the current and the torque of the IM under fault conditions as well as providing fault components. Hilbert transform and the support vector machine (SVM) multi-classification method are applied to improve the sensitivity of fault identification and eliminate the interference of the environmental electromagnetic noise. In order to increase the diagnosis accuracy in different applications, the grid search (GS), the genetic algorithm (GA) and the particle swarm algorithm (PSA) are employed for the parameter optimization of the SVM classification prediction model. The simulation and the experimental results verify the proposed analysis and diagnosis method.

Funding

National Natural Science Foundation of China (ZR2020YQ40)

History

Journal title

Conference Record - IAS Annual Meeting (IEEE Industry Applications Society)

Volume

2021-October

Language

English

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