Early fault identification method of rolling bearing under variable rotating speed working condition

Abstract

The invention relates to an early fault identification method of a rolling bearing under a variable rotating speed working condition. The method comprises the following steps that firstly, time-frequency analysis is carried out on a vibration signal of the rolling bearing to obtain a time-frequency spectrum; secondly, a peak value search method is adopted to extract an instantaneous frequency conversion in the time-frequency spectrum and data fitting is carried out; then high-pass filtering is carried out on an original vibration signal, and equal angle sampling is carried out on the originalvibration signal according to the instantaneous frequency conversion after data fitting to obtain an angle domain signal; finally, noise reduction processing is carried out on the original vibration signal and envelope spectrum analysis is carried out on the noise-reduced signal to identify faults of the rolling bearing. The method does not rely on a rotation speed sensor or human experience, andself-adaptively searches optimal parameters of the MCKD, moreover, the method is convenient and reliable, and is particularly suitable for vibration signal analysis of the rolling bearing with low signal-to-noise ratio under variable rotation speed working conditions.

Description

technical field [0001] The application relates to an early fault identification method for rolling bearings under variable speed conditions, which is applicable to the technical fields of mechanical monitoring and fault diagnosis. Background technique [0002] The variable speed working condition is a common working condition of rotating equipment in industrial production, such as the start and stop conditions of various rotating equipment, the variable speed operation of wind turbines, etc. are all variable speed working conditions. Rolling bearings are the core components of rotating machinery, and their health status directly affects the overall health of the machinery. In practice, equipment downtime caused by rolling bearing failures occurs from time to time, which not only affects normal production, but also some serious rolling bearing failures may even cause production safety accidents. It can be seen that identifying the early faults of rolling bearing faults and t...

AI Technical Summary

Problems solved by technology

First of all, this method realizes equiangular resampling of the vibration signal by calculating the time spectrum of the time domain vibration signal under variable speed conditions without relying on the speed sensor, and converts the non-stationary vibration signal in the time domain into a stationary signal in the angle domain. It solves the problem that MCKD cannot handle non-stationary vibration signals under variable speed conditions; then the adaptive MCKD method designed by the present invention is used to eliminate random noise in the early fault vibration signals of rolling bearings after equiangular sampling, avoiding the traditional MCKD method parameter Problems that are difficult to choose can effectively highlight the early fault characteristics of rolling bearings under variable speed conditions, which is conducive to the identification of early faults of variable speed rolling bearings

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