Induction motor bearing fault diagnosis system and diagnosis method based on wavelet-spectral kurtosis

Abstract

The invention discloses an induction motor bearing fault diagnosis system and diagnosis method based on wavelet-spectral kurtosis. The induction motor bearing fault diagnosis system based on the wavelet-spectral kurtosis comprises a wavelet-spectral kurtosis analysis module, a time domain parameter statistical analysis module, an FFT analysis module, a Hilbert envelope demodulation analysis module, a spectral kurtosis analysis module, and the like; the wavelet-spectral kurtosis analysis module includes two data preprocessing methods of wavelet and wavelet packet, which can realize the identification and feature extraction of multiple faults in a motor bearing coupled fault vibration signal; the time domain parameter statistical analysis module monitors whether the motor bearing is abnormalor not by calculating kurtosis values of time domain sensitive parameters; and a spectrum analysis module and the Hilbert envelope demodulation analysis module mainly determine the feature extractionof the single bearing fault of the motor. The induction motor bearing fault diagnosis system based on the wavelet-spectral kurtosis realizes the integrated design of the induction motor bearing faultdiagnosis method, and can quickly and effectively process and analyze the single and composite fault signals of the motor bearing and obtain accurate diagnosis results.

Description

technical field [0001] The invention relates to a vibration signal-based induction motor mechanical fault diagnosis technology, in particular to a wavelet-spectral kurtosis-based induction motor bearing fault diagnosis system and diagnosis method. Background technique [0002] As a high-precision, standardized core component of induction motors, bearings have the advantages of reducing frictional resistance between rotating parts, easy installation, strong interchangeability, convenient lubrication, and high reliability. However, due to the relatively harsh working environment of the bearing and the load action of various loads, it has become a high-frequency component that fails in electrical equipment. According to relevant statistics, bearing failure accounts for as high as 40% of all motor failures, and is one of the most common failures of induction motors. If the early faults of motor bearings can be monitored and diagnosed in a timely and effective manner, and repair...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem of difficult detection of motor bearing coupling faults, the present inventi...

Images