Fault diagnosis method for rolling bearing

Abstract

The invention discloses a fault diagnosis method for a rolling bearing. The method comprises the following steps that 1, the fault feature frequency of the rolling bearing is calculated; 2, an acceleration signal, to be detected, of the rolling bearing is obtained; 3, Hilbert transform is carried out on the acceleration signal to be detected, and a Hilbert envelope demodulation signal is calculated; 4, stochastic resonance treatment based on standard transform is carried out on the Hilbert envelope demodulation signal, and a carrier frequency and an output signal are obtained; 5, fast Fourier transform is carried out on the output signal to obtain an output signal frequency spectrum peak value, and the feature frequency of the acceleration signal to be detected is obtained according to the carrier frequency parameter and the output signal frequency spectrum peak value; 6, the feature frequency of the acceleration signal to be detected is compared with the fault feature frequency, and a diagnosis result is obtained. By means of the method, the fault of the rolling bearing can be diagnosed and recognized, and particularly, the early fault of the rolling bearing in the strong-noise background can be diagnosed and recognized.

Description

technical field [0001] The invention relates to the field of fault diagnosis. More specifically, it relates to a fault diagnosis method for rolling bearings. Background technique [0002] As an important rotating part in mechanical equipment, rolling bearing is also one of the important sources of failure of mechanical equipment. Statistics show that in rotating machinery using rolling bearings, about 30% of mechanical failures are caused by rolling bearings. Rolling bearing failures in induction motor failures It accounts for about 40% of motor failures, and the failure rate of rolling bearings in all kinds of gearbox failures is second only to gears and accounts for 20%. The data show that about 40% of the existing rolling bearings used in locomotives in our country have to go through the off-car inspection every year, and about 33% of them are replaced. Economic benefits and practical value. According to statistics, after applying condition monitoring and fault diagnos...

AI Technical Summary

Problems solved by technology

However, the normalized scaling stochastic resonance method and the subsampling stochastic resonance method require a very high sampling frequency (sampling frequency must be more than 50 times the target frequency), and the modulated stochastic resonance method requires a longer data length

These conditions hinder the application of stochastic resonance theory in signal processing engineering to a certain extent

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