A Fault Diagnosis Method for Rolling Bearings Based on Optimal Dimension Singular Spectrum Decomposition

Abstract

The invention discloses a rolling bearing fault diagnosis method based on optimal dimension singular spectrum decomposition, which comprises the following steps: step 1, installing an acceleration sensor near the rolling bearing seat to collect vibration signals; step 2, using optimal dimension singular spectrum decomposition The algorithm decomposes the collected vibration signal to obtain several singular spectral components with clear physical meaning; step 3, select the singular spectral component containing rich fault characteristic information as the principal component according to the kurtosis criterion; step 4, calculate the principal component The 1.5-dimensional frequency-domain weighted energy spectrum of the component components; step 5, observe whether there is an obvious peak at the fault characteristic frequency from the 1.5-dimensional frequency-domain weighted energy spectrum, so as to realize the accurate diagnosis of the rolling bearing fault. The invention is simple and easy to implement, and can more accurately realize the fault diagnosis of rolling bearings compared with other prior art.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis in mechanical equipment, in particular to a rolling bearing fault diagnosis method based on optimal dimension singular spectrum decomposition. Background technique [0002] Rolling bearings are the key components of most rotating machinery, and are widely used in various machinery because of their large load bearing and remarkable energy-saving effect. However, rolling bearings are often in harsh working conditions such as heavy loads and high speeds in use, and various failures will inevitably occur, which will affect the normal operation of the entire mechanical equipment. Therefore, timely detection of rolling bearing failures is of great significance to ensure the safe and stable operation of mechanical equipment. In addition, due to the change of the transient characteristics of the mechanical system during operation, non-stationary characteristics (such as time-varying frequency cha...

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Problems solved by technology

However, the embedding dimension in singular spectrum decomposition is selected according to empirical criteria, which may lead to mode aliasing and over-decomposition

In addition, there are more or less some in-band noise and interference in the singular spectral components obtained by singular spectrum decomposition

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