Early fault determining method for bearing

Abstract

The invention relates to an early fault determining method for a bearing. The method comprises the steps of cutting an acquired bearing vibration time domain signal into N groups of secondary vibration signals according to the same length; 2, calculating local spectral band energy Mg of each secondary vibration signal, namely, 1) performing frequency domain conversion for each secondary vibration signal; 2) selecting a local spectral band from the whole Hilbert marginal spectrum based on the formula shown in specification as the interval, wherein fp is the bearing fault characteristic frequency calculated based on the bearing structural dimension, and delta f is 2Hz; 3) calculating the local spectral band energy through the formula shown in the specification, wherein h(f) is frequency amplitude in the local spectral band; 3, creating a local spectral band energy sequence through N Mg, wherein the local spectral band energy value of each vibration signal is Mf when the bearing is free of a fault; Mg is not less than the product of K and Mf at M times in the sequence, wherein K is a constant; the characteristic power rate (CPR) is determined according the formula shown in the specification; 4, determining the early fault when the CPR is more than or equal to some constant A.

Description

technical field [0001] The invention relates to a vibration signal feature extraction method, in particular to a bearing early fault determination method. Background technique [0002] Bearings are one of the important components of rotating machinery equipment. In practical engineering applications, bearing failures have always been frequent faults in rotating machinery equipment. The extraction of early failure features of bearings can detect bearing failures early and avoid the failure of rotating machinery equipment caused by bearing failures. secondary damage. The bearing vibration signal contains a wealth of bearing fault information, and the detection and early warning of bearing faults can be realized by using the applicable feature extraction method to extract sensitive features. [0003] For rolling bearings, the bearing consists of a bearing outer ring, a bearing inner ring, balls and a cage. Bearing faults are also divided into four types: outer ring faults, in...

AI Technical Summary

Problems solved by technology

In practical engineering applications, there has been such a phenomenon for a long time, that is, there is no obvious fault characteristic frequency in the vibration signal spectrum, but the bearing has been found to have early faults during regular dismantling and inspection, and it is difficult to detect early bearing faults by using the general method of analyzing the characteristic frequency of bearing faults

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