Bearing fault diagnosis method based on synchronous optimization of wavelet filter and MCKD by using NGAs

Abstract

The invention discloses a bearing fault diagnosis method based on synchronous optimization of wavelet filter and MCKD by using NGAs. The method comprises the steps: firstly, collecting an original vibration signal with a sensor and inputting the original vibration signal; setting an initial condition of an ecological niche genetic algorithm (NGAs); synchronously and jointly optimizing the centralfrequency and bandwidth of a Morlet wavelet filter and the length and period of a maximum correlation kurtosis deconvolution (MCKD) filter by using NGAs; taking correlation kurtosis (CK) of bearing fault impact period generation characteristics as an optimization index, and realizing parameter adaptive synchronization optimization of the front and back two processing steps; conducting in-band noise reduction processing on filtering signals by adopting Morlet band-pass filtering preprocessing and MCKD; and finally, judging whether faults exist or not and fault types through an envelope spectrumof the signals obtained after MCKD in-band noise reduction. Analysis of simulation signals, laboratory signals and experimental data shows that the method provided by the invention can effectively eliminate external accidental interference impact influences and reduce signal transmission paths and noise influences, and the effectiveness of bearing fault diagnosis is ensured.

Description

technical field [0001] The invention relates to a NGAs synchronously optimized wavelet filter and MCKD bearing fault diagnosis method, which belongs to the technical field of rolling bearing fault diagnosis. Background technique [0002] As one of the most important components of rotating machinery, rolling bearings are widely used in important fields such as machinery, transportation, aerospace, etc., and are also the most prone to failure due to harsh working conditions. Once the rolling bearing fails and is not found in time, it may cause immeasurable consequences. Therefore, how to accurately judge the health status of rolling bearings is very important to improve the reliability and availability of mechanical equipment and ensure the safe operation of equipment. However, vibration signals are often submerged in strong background noise and occasional high-amplitude interference shocks, making it difficult to extract fault feature information. Therefore, the key to accu...

AI Technical Summary

Problems solved by technology

[0003] The optimization index used in the processing of band-pass filtering and in-band noise elimination does not take into account the periodic occurrence characteristics of bearing transient fault shocks, making it susceptible to occasional interference shocks and the two processing steps are independently optimized, which is difficult to guarantee diagnosis The overall effect of the problem, the present invention proposes a kind of Morlet wavelet filtering and maximum correlation kurtosis deconvolution (MCKD) parameters synchronously optimized bearing fault diagnosis method

Images