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
• DOI: 10.3390/s22228749
• ISSN: 1424-8220

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 of rotatory machinery that combines time-frequency feature oversampling (TFFO) with a convolutional neural network (CNN). First, the sliding segmentation sampling method is employed to primarily increase the number of fault samples in the form of one-dimensional signals. Immediately after, the signals are converted into two-dimensional time-frequency feature maps by continuous wavelet transform (CWT). Subsequently, the minority samples are expanded again using the synthetic minority oversampling technique (SMOTE) to realize TFFO. After such two-fold data expansion, a balanced data set is obtained and imported to an improved 2dCNN based on the LeNet-5 to implement fault diagnosis. In order to verify the proposed method, two experiments involving single and compound faults are conducted on locomotive wheel-set bearings and a gearbox, resulting in several datasets with different imbalanced degrees and various signal-to-noise ratios. The results demonstrate the advantages of the proposed method in terms of classification accuracy and stability as well as noise robustness in imbalanced fault diagnosis, and the fault classification accuracy is over 97%.