Locomotive wheelset bearing fault diagnosis method based on angular domain-time domain-frequency domain

Abstract

The invention discloses a locomotive wheelset bearing fault diagnosis method based on the angular domain-time domain-frequency domain, and belongs to the technical field of bearing testing. The method includes the steps that firstly, collected vibration acceleration signals of locomotive wheelset bearings are analyzed in the time domain and the frequency domain, whether the bearings are in a fault state or not is judged, the specific portions of the faults are judged, angular domain analysis is conducted on vibration acceleration creatively, and quantitative analysis data of the faults are acquired, so that the severity degrees of the faults are grasped, and basic data are provided for further fault monitoring. The analysis method in which the time domain, the frequency domain and the angular domain are combined is adopted, information such as fault occurrence, fault types and fault severity degrees of the bearings can be accurately provided, especially, the angular domain fault diagnosis method is creatively provided, an accurate monitoring mode is provided for the fault development degree, and more abundant reference data are provided for subsequent fault processing.

Description

technical field [0001] The invention belongs to the technical field of bearing testing. Background technique [0002] At present, the fault diagnosis methods of rotating machinery are becoming more and more mature, such as oil analysis method, ultrasonic flaw detection method, ferrography analysis method, temperature detection method, vibration analysis method and so on. Among these methods, the vibration analysis method has been widely used because of its high fault identification rate and simple and rapid detection method. At present, around the vibration analysis method, experts and scholars at home and abroad have developed a series of fault diagnosis algorithms, including wavelet analysis, empirical mode decomposition, shock pulse method, resonance demodulation method, time-frequency analysis method, etc. Although these methods have a high fault identification rate, most of them are used under harsh conditions, and are only effective for stable conditions, and are less...

AI Technical Summary

Problems solved by technology

However, this method of fault diagnosis is cumbersome, and the diagnostic accuracy is low, and it is impossible to determine the bearing fault location and quantitative judgment results

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