Rolling Bearing Fault Diagnosis Based on a Synchrosqueezing Wavelet Transform and a Transfer Residual Convolutional Neural Network

Rolling Bearing Fault Diagnosis Based on a Synchrosqueezing Wavelet Transform and a Transfer Residual Convolutional Neural Network

This study proposes a novel rolling bearing fault diagnosis technique based on a synchrosqueezing wavelet transform (SWT) and a transfer residual convolutional neural network (TRCNN) designed to address the difficulties of feature extraction caused by the non-stationarity of fault signals, as well a...

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Bibliographic Details
Main Authors: Zihao Zhai, Liyan Luo, Yuhan Chen, Xiaoguo Zhang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Sensors
Subjects:
fault diagnosis
synchrosqueezing wavelet transform (SWT)
transfer learning
transfer residual convolutional neural network (TRCNN)
Online Access:https://www.mdpi.com/1424-8220/25/2/325
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