Dual-Path Mixed-Domain Residual Threshold Networks for Bearing Fault Diagnosis

• Published in: IEEE transactions on industrial electronics (1982) Vol. 69; no. 12; pp. 13462 - 13472
• Main Authors: Chen, Yongyi, Zhang, Dan, Zhang, Hui, Wang, Qing-Guo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Asynchronous motors; Convolution; Convolutional neural networks; Deep learning; Dilated convolution; Domains; Fault diagnosis; Feature extraction; Interference; Machine learning; mixed-domain mechanism; Neural networks; Roller bearings; rolling bearings; soft threshold; Task analysis; Vibrations; White noise

Intelligent bearing fault diagnosis based on deep learning is one of the hotspots in mechanical equipment monitoring applications. However, traditional deep learning-based methods have a weak antinoise ability and poor generalization performance in a noisy environment. This article presents a new si...