Rolling bearing weak fault feature early extraction method

Abstract

The invention discloses a rolling bearing weak fault feature early extraction method. The method includes the following steps that: a sensor is utilized to pick up the vibration signals of a rolling bearing under an operating condition, and the vibration signals are adopted as signals to be analyzed; with the spectrum auto-correlation feature factor SACFF of a spectrum auto-correlation function adopted as a fitness function, a genetic algorithm is adopted to optimally search variation modal decomposition parameters; parameter combinations which are optimally searched by the genetic algorithm are selected to perform VMD (variation modal decomposition) processing on the signals to be analyzed, so that finite bandwidth intrinsic mode functions are obtained; components corresponding to local maximum feature factors of spectrum autocorrelation are selected to be subjected to spectrum autocorrelation analysis, so that a spectrum autocorrelation function graph can be obtained; and if the fault feature frequency in the spectrum autocorrelation function graph or the peak value of the frequency multiplication thereof reaches a set threshold value, it is indicated that an early weak fault occurs on the rolling bearing. According to the method of the invention, the respective advantages of the VMD and the spectrum autocorrelation analysis are combined, and therefore, limitations of the spectrum autocorrelation analysis method in extracting the weak fault feature information of the bearing can be broken through, and the earlier diagnosis of the weak fault of the rolling bearing can be realized.

Description

technical field [0001] The invention relates to an early extraction method for weak fault features of rolling bearings, more specifically an early extraction method for weak fault features of rolling bearings based on the combination of parameter optimization variational mode decomposition and frequency spectrum autocorrelation analysis. Background technique [0002] Rolling bearings are important basic components of mechanical equipment, and whether their working status is normal or not directly affects the operation safety of the equipment; generally speaking, rolling bearings will always experience normal operation, early weak failures, and serious failures in the operating range of their entire life; early detection Weak mechanical failure characteristics of rolling bearings and timely prediction can avoid major economic losses and casualties. The characteristic signals of the early mechanical fault incubation period of rolling bearings are weak and secondary. In additio...

AI Technical Summary

Problems solved by technology

[0006] In order to avoid the shortcomings of the above-mentioned prior art, the present invention provides a method for early extraction of weak fault features of rolling bearings, which overcomes the difficulty in selecting VMD parameters and the difficulty of extracting weak fault features in spectral autocorrelation analysis, and combines the two to realize rolling bearings. Early extraction of weak fault features achieves the purpose of diagnosing weak faults earlier

Images