Wearing fault diagnosis method of automobile engine bushing

Abstract

A wearing fault diagnosis method of an automobile engine bushing comprises that vibration signals in normal and bushing wearing conditions of an automobile engine are collected, and time-domain statistics feature extraction is carried out; frequency-domain feature extraction is carried out on the vibration signals via a signal processing method of wavelet packet transformation; a kurtosis index ismixed with a wavelet packet energy entropy to obtain a characteristic vector; and a classification model on the basis of a K-nearest neighbor algorithm s established, the characteristic vector is used to train and test the classification model, and thus, the wearing fault of the single bushing of the automobile engine is diagnosed. The method has the advantages that the bushing wearing fault canbe analyzed in focus among common faults of the automobile engine, sufficient and effective characteristic value extraction can be realized for typical faults of the automobile engine, and the fault type can be identified in high accuracy.

Description

technical field [0001] The invention belongs to the technical field of automobile engine bearing wear fault diagnosis based on vibration signals, and specifically relates to a signal processing method based on time domain kurtosis fusion wavelet packet energy entropy to extract engine bearing wear fault features and establish a K-based nearest neighbor algorithm The classification model implements the fault pattern recognition method for the fault diagnosis of automobile engine bearing wear. Background technique [0002] In the whole vehicle, the engine, as the core component of the car, is the source of power for the car. At the same time, the overall performance of the car engine is closely related to the overall performance of the vehicle. Due to the relatively harsh working environment of the engine, and the extremely complex composition of the mechanical part and the electronic control system, according to the survey data of relevant agencies, the probability of its fa...

AI Technical Summary

Problems solved by technology

On the one hand, the fault samples collected in the experiment are insufficient, the correct rate of fault identification is not high enough, or the training time of the classification network is too long

On the other hand, the extraction of feature quantities is insufficient, and the types of identifiable faults are relatively small. Most of the research is only carried out on a specific type of engine.

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