Gray fault diagnosis method for key rotating part of vehicle driven by signal frequency domain characteristics

Abstract

The invention relates to the technical field of mechanical equipment signal processing, in particular to a gray fault diagnosis method for a key rotating part of a vehicle driven by signal frequency domain characteristics. According to the characteristic that vibration signal frequency domain characteristics caused by different faults are different, frequency response of Q-switched wavelet transform is used for analyzing vehicle rotating part vibration signals or envelope signals of the vehicle rotating part vibration signals, and feature vectors with sub-band energy proportions of the signals in different frequency bands as elements are constructed; for the problem that the correlation degree is too large due to the fact that the change trends of the two sequences are different and the original cumulative difference is small when the gray proximity correlation degree processes the two groups of intersecting sequences, a gray absolute proximity correlation degree model is provided; and under the driving of the constructed frequency domain feature vector, the gray absolute proximity correlation degree of the frequency domain feature vector and a standard mode is calculated to identify the fault state of the key rotating part of the vehicle. Compared with other vehicle key rotating part fault diagnosis methods, the method has the advantages of theoretical support and efficient algorithm.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to a gray fault diagnosis method for a key rotating part of a vehicle driven by signal frequency domain characteristics. Background technique [0002] Fault diagnosis technology is a key technology to improve the effectiveness of mechanical equipment. It determines the health status of equipment by detecting, extracting and identifying the operating status information of mechanical equipment. As vehicles play an increasingly important role in transportation and industrial production, the failure and damage of vehicles, especially the failure of key rotating components at high speeds and high loads, may cause serious disasters and losses. Therefore, the fault diagnosis of the key rotating parts of the vehicle plays a vital role in the reliability and safety of the vehicle operation. However, most of the current fault diagnosis methods require a large amount of experimental data, and lack ...

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Problems solved by technology

[0003] The present invention aims to solve the above-mentioned defects. According to the characteristics of different frequency domain characteristics of vibration signals caused by different faults, the frequency response of Q-switched wavelet transform is used to analyze the vibration signals of the rotating parts of the vehicle or their envelope signals, and construct a signal in different frequency domains. The energy ratio of each sub-band in the frequency band is the eigenvector of the element; in view of the problem that the gray proximity correlation degree deals with two sets of intersecting sequences, the two sequences have different changing trends, but the original cumulative difference is small and the correlation is too large. A gray absolute proximity correlation model is established; driven by the constructed frequency domain eigenvector, calculate its gray absolute proximity correlation with the standard model to identify the fault status of the key rotating parts of the vehicle

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