Self-diagnosis method for faults of rolling bearing retainer

Abstract

The invention discloses a self-diagnosis method for faults of a rolling bearing retainer. The method comprises steps of rapidly filtering vibration signals through a high-precision rapid filter bank;selecting an optimal signal according to the spectral kurtosis of the mean square envelope autocorrelation signal of each filtered signal, and calculating an envelope spectrum of the optimal signal; then, automatically selecting the first M-order actual fault characteristic frequency according to the theoretical fault characteristic frequency of the retainer; automatically finding out a thresholdvalue meeting the probability requirement according to the statistical characteristic of the frequency spectrum; and finally, calculating a global test index and a fault occurrence rate of cyclic stability, and realizing self-diagnosis of the fault of the rolling bearing retainer. According to the invention, an effective self-diagnosis tool is provided for an intelligent bearing to analyze the fault of the rolling bearing retainer.

Description

technical field [0001] The invention relates to the field of fault diagnosis technology and signal processing and analysis technology, in particular to a self-diagnosis method for a rolling bearing cage fault. Background technique [0002] The cage is an important part of the rolling bearing, and its failure seriously affects the normal operation of the rolling bearing. In order to diagnose the fault of the cage in time, the diagnosis can be completed by analyzing its vibration characteristics. However, the fault signal of the cage is usually weak and easily submerged in the background noise. It is necessary to carry out relevant signal processing to weaken the noise interference. In addition, traditional diagnosis usually requires expert decision-making, and the diagnosis result is closely related to expert experience, and there is a problem of time cost. Therefore, it is necessary to develop a self-diagnosis method for cage failure. [0003] The high-precision fast filt...

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Problems solved by technology

However, this method is easy to introduce periodic interference in the signal when used alone

In addition, when the rolling bearing cage fault characteristic signal is extracted, its diagnosis usually needs to be identified by experts, which is not conducive to real-time online diagnosis

In order to overcome this problem, the theoretical fault characteristic frequency of the cage can be used for identification, but due to the influence of slip factors, the actual fault characteristic frequency is often not equal to the theoretical fault characteristic frequency, and it is difficult to effectively complete self-diagnosis

And because the collected signal is interfered by noise, the effect of self-diagnosis of cage fault directly on the spectrum of the original signal is poor

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