Bearing fault diagnosis method based on fault impact extraction and autocorrelation overall average

Abstract

The invention discloses a bearing fault diagnosis method based on fault impact extraction and autocorrelation overall average. The method separates large amplitude impact transient (LAIT) signal segments from vibration signals of a bearing rolling body, the separated LAIT signal segments are subjected to band-pass filtering and envelope demodulation, and the phases of the LAIT signal segments arealigned through an autocorrelation function; and then, the aligned LAIT signal segments are subjected to overall average, linear trend term elimination and regularization, and enhanced feature signalsLAIT-AEA are generated to judge whether a fault exists in the bearing. The bearing fault diagnosis method disclosed in the invention does not need a tachometer and an optical encoder, is insensitiveto the rotation speed fluctuation and the rolling body sliding and has the characteristics of high fault sensitivity and small calculation amount.

Description

technical field [0001] The invention relates to the technical field of rolling bearing fault diagnosis, in particular to a bearing fault diagnosis method based on fault impact extraction and autocorrelation overall average. Background technique [0002] Fault diagnosis of rotating machinery can provide support for maintenance decisions, help prevent mechanical failure, and avoid major losses caused by mechanical failure. Fault diagnosis of rotating machinery has been active for decades, and a variety of effective signal processing methods have emerged. Features that are sensitive to faults are extracted in the case of low SNR. [0003] Time Synchronous Averaging (TSA) is a widely recognized signal preprocessing method to achieve enhanced diagnosis by improving the signal-to-noise ratio of characteristic signals. TSA divides a long signal x(t) into continuous signal segments, and the signal length is the period of the target characteristic signal. A synchronized overall ave...

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Problems solved by technology

[0008] Second, phase errors between separated signal segments can also cause loss of diagnostic information

Superimposing such oscillation waveforms, even a phase error of only 1 / 4 of the natural frequency period will cause a large loss of information, such as figure 1 (c) as shown

Moreover, since the natural frequency of the bearing is often as high as thousands or even thousands of Hz, even as low as 2.5×10 -4 The phase error of s, the information loss caused by it is also not negligible

[0009] Third, the rotational speed signal of the bearing cannot be used as a trigger signal to perform TSA

But this averaging method can only be used to detect inner ring faults, and the cage frequency needs to be measured, and it is not feasible in engineering to use embedded sensors to measure the cage frequency inside the bearing

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