Rolling bearing fault detection method based on cascade multistable stochastic resonance and empirical mode decomposition (EMD)

Abstract

The present invention relates to a rolling bearing fault detection method based on cascade multistable stochastic resonance and EMD. The method comprises the steps of calculating the fault characteristic frequency of a to-be-diagnosed mechanical device, extracting the vibration data of the to-be-diagnosed mechanical device as the input of a cascade multistable stochastic resonance system, wherein the output of the cascade multistable stochastic resonance system is used as a vibration data result corresponding to the vibration data; carrying out the Fourier transform on the extracted vibration data result to obtain the frequency spectrum of an original signal, and determining the frequency components contained in the frequency spectrum; optimally selecting the parameters of the multistable stochastic resonance system, and introducing a vibration signal in the cascade multistable stochastic resonance system; taking the output of the last-level of the cascade multistable stochastic resonance system as the output of the cascade multistable stochastic resonance system, carrying out the EMD on the output of the system, extracting the frequency components contained in the signals and according with the prescient fault characteristic frequency, and determining whether the rolling is faulted and the faulted parts according to an EMD result.

Description

technical field [0001] The invention relates to the technical field of rolling bearing fault diagnosis, in particular to a rolling bearing fault detection method based on cascaded multi-stable stochastic resonance and EMD. technical background [0002] Rolling bearing is one of the most widely used mechanical parts, and it is also one of the most easily damaged components in mechanical equipment. Its operating status directly affects the function of the entire equipment. According to incomplete statistics, in rotating machinery using rolling bearings, about 30% of mechanical failures are caused by bearings. Bearing failures are caused by fatigue spalling, wear, plastic deformation, corrosion, fracture, gluing, and cage damage. If the early failure of bearings is not diagnosed in time, it will lead to serious failure of machinery and equipment, resulting in huge economic losses. Therefore, diagnosing the early fault characteristics of bearings has great practical significan...

AI Technical Summary

Problems solved by technology

[0005] In fact, when dealing with weak signals with extremely low signal-to-noise ratio, the effect of single stochastic resonance cannot achieve our expected goal, which will have a certain impact on the accuracy of diagnostic results

Images