Failure mode recognition method of epicyclic gearbox using mixed domain feature vector and grey correlation analysis

Abstract

The invention discloses a failure mode recognition method of an epicyclic gearbox using a mixed domain feature vector and grey correlation analysis. The method comprises the steps that (1) the feature parameters ft, ff, fs and ftf of the time domain, the frequency domain, the order domain and the time-frequency domain are extracted to constitute the mixed domain feature vector Fm, and the weighted values of wt, wf, ws and wtf of all the feature parameters are calculated; (2) the vibration monitoring health state of the epicyclic gearbox and the historical data of various failure mode states are acquired, and the corresponding mixed domain feature vector value mj(k) is calculated to be used as a reference state matrix; (3) the feature vector t of a signal to be detected and the row vector of the reference state matrix m are both used as the input of a grey correlation analysis algorithm; (4) the correlation vector of the feature vector t of the signal to be detected and the standard mode matrix m is calculated to be used as an output vector of the grey correlation analysis algorithm. The failure mode recognition method of the epicyclic gearbox using the mixed domain feature vector and grey correlation analysis has the advantages of being simple in principle, simple and convenient to operate, stable and reliable, high in precision, suitable for high interference and varied working conditions and the like.

Description

technical field [0001] The invention mainly relates to the technical field of gearbox failure prediction and identification, in particular to a method for identifying a failure mode of a planetary gearbox by using a mixed domain eigenvector and gray relational analysis. Background technique [0002] In the prior art, the early damage of the planetary gearbox is often detected by a single time-domain index, spectrum analysis or time-frequency analysis method, and its failure modes such as gear missing teeth, pitting, tooth root cracks, etc. are identified. In the above-mentioned technology, the vibration signal of the planetary gearbox acquired by the sensor is pre-processed to reduce noise, and then the corresponding characteristic parameters are calculated through time-domain statistical analysis, fast Fourier transform or time-frequency analysis method, and then through setting a single The fault / damage is detected by the threshold value of the class characteristic paramet...

AI Technical Summary

Problems solved by technology

[0003] Generally speaking, there are a lot of noise and other power frequency interference in the working environment of the planetary gearbox. It is often difficult to overcome the above interference factors by using single-type characteristic parameters for fault mode identification, and it is also unable to adapt to changes in working conditions such as speed and torque. And then cause high false alarm or false alarm, affecting the normal operation of the equipment

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