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Fault diagnosis model of rolling bearing based on synchro squeezing wavelet transform and Transformer

Published:2024-06-27 author:ZHANG Xiangyu, WANG Yanxue. Browse: 491 Check PDF documents

Fault diagnosis model of rolling bearing based on synchro squeezing wavelet

transform and Transformer

ZHANG Xiangyu, WANG Yanxue

( School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering

and Architecture, Beijing 100044, China)

Abstract: A rolling bearing intelligent fault diagnosis method based on synchronous squeezing wavelet transform (SST)-Transformer was proposed to address the problem of insufficient utilization of fault information and difficulty in extracting features, when using neural networks for fault diagnosis of rolling bearings. Firstly, synchronous squeezing wavelet transform was used as the signal processing module, the one-dimensional vibration signal was transformed into a time-frequency map. Next, a time-frequency map segmentation method was designed to preserve fault information to the greatest extent possible, dividing the time-frequency map into a series of image block sequences. Then, the sequence was input into a Transformer model with strong processing capabilities for sequence data, and feature extraction was performed. Finally, the feature data was input into the classifier for classification, and the diagnostic performance of different timefrequency map segmentation methods was compared. The SST Transformer model was also compared with the benchmark algorithm. The research results show that compared to other segmentation methods, the intelligent fault diagnosis method for rolling bearings based on SST Transformer has improved the diagnostic accuracy by 3.45% and significantly improved the convergence speed of model training. Comparing to other benchmark algorithm, the average accuracy has improved by at least 1.05%. The method has high diagnostic accuracy and good stability.

Key words: intelligent fault diagnosis; neural network(NN); fault feature extraction; attention mechanism; deep learning; synchro squeezing wavelet transform(SST); Transformer model

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