Fault Detection of Induction Motor Using Fast Fourier Transform with Feature Selection via Principal Component Analysis

Fault detection and diagnosis of the induction motor is important to prevent the system downtime of industrial fields. Most of the fault detection and diagnosis is conducted in the frequency domain using fast Fourier transform (FFT). Although several studies have been done using FFT, this method has difficulties in finding the fault characteristic frequency component. To overcome these difficulties, this paper provides the algorithm using principal component analysis (PCA) to easily find the feature of the FFT signal and utilize the Hotelling’s $$T^{2}$$ as an index for fault detection. After selecting the peak of top five frequencies and corresponding amplitude of the FFT as a feature and reducing the dimension through PCA, it is possible to detect a motor abnormality through Hotelling’s $$T^{2}$$ value. The proposed method is verified for detecting abnormal states of three-phase squirrel-cage induction motor. It has been confirmed that using the fault characteristic frequency component of both frequency and corresponding amplitude is more accurate in determining the motor abnormality than the characteristic of only frequency.

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