Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 70, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2021.3119135
Keywords
Bearing fault diagnosis; conditional generative adversarial network (CGAN); convolutional neural network; deep learning; small samples
Funding
- National Natural Science Foundation of China [51905422]
- Natural Science Basic Research Program of Shaanxi [2020JQ-630]
- China Postdoctoral Science Foundation [2020M673613XB]
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The proposed CGAN-2-D-CNN fusion diagnosis model is effective in diagnosing bearing faults with small sample data, showing close accuracy to the 2-D-CNN model directly used on a larger original sample size. The 2-D-CNN model after 2-D preprocessing also demonstrates higher fault classification accuracy compared to other models like 1-D-CNN, SVM, and LSTM.
The rolling bearing is the key component of rotating machinery, and it is also a failure-prone component. The intelligent fault diagnosis method has been widely used to accurately diagnose bearing faults. However, in engineering practice, it is difficult to obtain sufficient sample data to train the intelligent diagnosis model. Therefore, in this article, a fusion diagnosis model CGAN-2-D-CNN that combines a conditional generative adversarial network (CGAN) and a two-dimensional convolutional neural network (2-D-CNN) is proposed for bearing fault diagnosis with small samples. Considering the problem of insufficient sample data, CGAN is used to learn the data distribution of real samples to generate new samples with similar data distribution by the confrontation training of the generator and discriminator. Then, 2-D preprocessing is conducted to convert the generated 1-D data into 2-D gray images. Finally, these images are input into the 2-D-CNN to extract the features and classify the bearing fault types. Two experimental cases are implemented to validate the effectiveness and feasibility of the proposed CGAN-2-D-CNN. The experimental results illustrate that the diagnosis accuracy of the proposed method used on the small sample data is close to that of the 2-D-CNN directly used on the enough original sample data whose size is equal to the expanded sample size. In addition, compared with the 1-D-CNN, support vector machine (SVM), and long short-term memory (LSTM) models, the 2-D-CNN model after 2-D preprocessing has the higher fault classification accuracy.
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