Motor Fault Diagnosis Based on Wavelet Transform - Lijun Wang; Huijuan Guo; Shenfeng Zhang (Indonesian Journal of Electrical Engineering and Computer Science, 2013)
The wavelet transform theory is used to motor fault diagnosis in this paper, considering its characteristics of multi-resolution and stronger feature extraction ability than Fourier. The paper emphasizes de-noising and eliminating the singular value point of the wavelet transform in the non-stationary signal. And it makes a detailed and in-depth analysis about how to detect the frequency components of weak signal by using equivalent power spectrum of reconstruction signal, which is acquired by using the wavelet transform. Through the comparison analysis of the simulation signal and motor vibration signal’s experimental data, the corresponding energy of original signal’s equivalent power spectrum and reconstructing signal’s equivalent power spectrum are compared to determine the fault frequency, so as to accurately find out the motor fault. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3288