Noise diagnosis algorithm for rolling bearing faults of rotary equipment

Abstract

The invention discloses a noise diagnosis algorithm for rolling bearing faults of rotary equipment. Firstly, a sound pick-up device collects running noise signals of a rolling bearing, and the signalsare subjected to preliminary fault judgment through a bearing normality and anomaly pre-classification model based on an anomaly detection algorithm; secondly, according to a fault pre-judgment result, the abnormal signals (the faults occur) pass through a neural network filter to filter normal components in the signals of the bearing, the output net abnormal signals are connected to a subsequentfeature extraction module, and the normal signals (no faults occur) are directly connected to the feature extraction module; the feature extraction module extracts Mel-cepstrum coefficients (MFCC) ofthe signals to serve as eigenvectors, feature reconstruction is carried out by utilizing a gradient boosted decision tree (GBDT) to form composite eigenvectors, and principal component analysis (PCA)is used for carrying out dimensionality reduction on features; and finally, feature signals are input into an improved two-stage support vector machine (SVM) ensemble classifier for training and testing, and at last, high-accuracy fault type diagnosis is achieved. According to the algorithm, the bearing faults can be effectively detected and relatively high fault identification accuracy is kept;and the algorithm has relatively high effectiveness and robustness for detection and classification of the bearing faults.

Description

technical field [0001] The invention relates to a method for classifying bearing faults, in particular to a fault noise diagnosis algorithm for rotating equipment rolling bearings. Background technique [0002] Bearings are one of the main components that are often prone to failure in the operation of rotating machinery. Due to the wear, turning and aging of bearings during mechanical operation, it will cause serious consequences to the operation of the entire system, not only causing huge economic losses to the enterprise, but also endangering the lives of employees and even the surrounding ecological environment. . Therefore, the health of the bearing is directly related to the safety of the machine and the effective operation of the system. The consequences of some bearing failures may be catastrophic, and it is necessary to detect and diagnose these failures in time at an early stage. [0003] Mechanical equipment will generate noise during operation. Compared with vib...

AI Technical Summary

Problems solved by technology

Due to the wear, turning and aging of bearings during mechanical operation, it will cause serious consequences to the operation of the entire system, not only causing huge economic losses to the enterprise, but also endangering the lives of employees and even the surrounding ecological environment.

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