Fault diagnosis method of variable-speed bearing

Abstract

The invention discloses a fault diagnosis method of a variable-speed bearing, which comprises the following steps of: sampling the vibration signals of the bearing at equal time intervals through an acceleration sensor by a data acquisition module controlled by a fault diagnosis module to obtain a vibration signal sequence x(n); carrying out wavelet transformation on the acquired vibration signal sequence x(n) by adopting a plurality of Morlet wavelets as the wavelet basis functions of the wavelet transformation to obtain wavelet coefficient wt(m,n); carrying out modular operation on the wavelet coefficient wt(m,n) to acquire the envelope ewt(m,n)=||wt(m,n)|| of the wavelet coefficient wt(m,n); transforming the wavelet envelope coefficient at each size from an equal time interval sampling result to an equal angle sampling result; carrying out Fourier transformation on the wavelet envelope sequences ewt(m,t) at various sizes, which are subjected to equal angle sampling, to obtain the frequency spectra eswt (m,f)=FFT(ewt(m,t)) of the wavelet envelope sequences ewt(m,t); and making eswt(m,f) into a three-dimensional image.

Description

technical field [0001] The invention relates to a fault diagnosis method for variable speed bearings. Background technique [0002] Modern industry's demand for high-quality and low-cost products and the emphasis on safety and continuity in the production process have shifted the focus of maintenance strategies for mechanical equipment from the initial after-the-fact maintenance and preventive maintenance to the current condition-based maintenance. In order to implement a condition-based maintenance strategy, real-time detection, diagnosis and prediction of the health status of mechanical equipment is required. Therefore, the corresponding health monitoring of mechanical equipment has become one of the most active research focuses in the industry and academia. As one of the main equipment types, rotating machinery equipment, its key component bearing, has become the main research object due to its wider application in the fields of sustainable energy production, processing a...

AI Technical Summary

Problems solved by technology

However, as a time-frequency domain method, the wavelet transform algorithm does not make full use of some important frequency-domain features contained in the signal, so there is a deviation in the diagnosis of the fault frequency; in addition, the existing fault diagnosis algorithms generally require that the equipment speed is constant or Close to constant, not suitable for fault diagnosis of bearings under variable speed conditions

There are currently no public solutions to the above two problems

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