Rolling bearing sound signal multiband fusion fault diagnosis method based on RLS and RSSD

Abstract

The invention discloses a rolling bearing sound signal multiband fusion fault diagnosis method based on RLS and RSSD. The rolling bearing sound signal multiband fusion fault diagnosis method comprises the following steps that firstly, a bearing operation sound signal is subjected to dereverberation processing through an RLS algorithm; secondly, noise reduction processing is conducted on low-frequency signals and high-frequency signals in the reverberation-removed signals through a resonance sparse decomposition method based on the optimal quality factor and a wavelet noise reduction algorithm; the signal amplitudes of the two frequency bands are compared, and the signals are amplified and superposed to complete signal reconstruction; and finally, the bearing fault is judged through envelope spectrum analysis. According to the method, the interference of noise and reverberation on the bearing signals is reduced, the weak bearing signals are extracted through a multi-band noise reduction method, and compared with an existing acoustic diagnosis method which is based on a single band and does not consider the reverberation influence, the method has the advantages of being good in noise suppression effect, small in signal effective information loss, high in diagnosis precision and the like. The method can accurately identify bearing faults, and is suitable for acoustic diagnosis of rolling bearings.

Description

technical field [0001] The invention relates to the field of rolling bearing fault diagnosis, in particular to a rolling bearing acoustic signal multi-band fusion fault diagnosis method based on RLS and RSSD. Background technique [0002] Rolling bearings are one of the most critical parts of rotating machinery. They play a role in supporting the rotation of rotating parts. They are also the most easily damaged parts of all mechanical parts. Among the rotating faults, 30% are caused by bearing failures. Reasonable and effective analysis and processing of acoustic signals generated during bearing operation is the key to bearing fault diagnosis. Improper acoustic signal processing methods will lead to failure to extract the frequency of bearing defects and reduce the accuracy of diagnosis results. [0003] At present, the rolling bearing fault diagnosis methods based on acoustic signals only focus on single frequency band signals, and most of them do not take into account the ...

AI Technical Summary

Problems solved by technology

Reasonable and effective analysis and processing of the acoustic signal generated during the operation of the bearing is the key to bearing fault diagnosis. Improper acoustic signal processing methods will lead to failure to extract the frequency of bearing defects and reduce the accuracy of diagnosis results.

[0003] At present, the rolling bearing fault diagnosis methods based on acoustic signals only focus on single frequency band signals, and most of them do not consider the interference of the reverberation effect caused by multiple reflections on the direct acoustic signal, resulting in the lack of effective information of the bearing and the distortion of the collected signal.

The existing methods do not effectively process the signal according to the generation mechanism of the bearing acoustic signal, the law of sound propagation attenuation and the characteristics of the transmission path, and the diagnostic results are greatly disturbed by environmental noise and have low reliability.

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