A self-diagnosis method for rolling bearing cage failure

Abstract

A self-diagnosis method for rolling bearing cage faults, fast filtering of vibration signals through high-precision fast filter banks, and then selecting the optimal signal according to the spectral kurtosis of the mean square envelope autocorrelation signal of each filtered signal and calculating Its envelope spectrum, and then automatically select the first M-order actual fault characteristic frequency according to the theoretical fault characteristic frequency of the cage, and then automatically find out the threshold that meets the probability requirement according to the statistical characteristics of the spectrum, and finally calculate the cyclostationary global test index and fault occurrence rate, The self-diagnosis of the fault of the rolling bearing cage is realized, the invention provides a method for automatically diagnosing the fault of the rolling bearing cage, and provides an effective self-diagnosis tool for the intelligent bearing to analyze the fault of the rolling bearing cage.

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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