An Imbalanced Fault Diagnosis Method Based on TFFO and CNN for Rotating Machinery

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 22; p. 8749
• Main Authors: Zhang, Long, Liu, Yangyuan, Zhou, Jianmin, Luo, Muxu, Pu, Shengxin, Yang, Xiaotong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.11.2022; MDPI
• Subjects: Algorithms; continuous wavelet transform; convolution neural network; data expansion; Datasets; Deep learning; Failure; Fault diagnosis; imbalanced data; Industrial production; Locomotives; Machinery; Magneto-electric machines; Methods; Neural networks; Signal processing; synthetic minority oversampling technique; Wavelet transforms; Germany; imbalanced data; data expansion; continuous wavelet transform; synthetic minority oversampling technique; convolution neural network

Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples are scarce in practice, posing a considerable challenge for existing diagnosis approaches to achieve highly accurate fault detection in real applications. This paper proposes an imbalanced fault diagnosis o...