期刊
IEEE-ASME TRANSACTIONS ON MECHATRONICS
卷 23, 期 1, 页码 101-110出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2017.2728371
关键词
Convolutional neural networks (CNNs); fault diagnosis; feature learning; rotating machinery; sensor fusion
类别
资金
- Canada Research Chair in Mechatronics and Industrial Automation
- Canada Foundation for Innovation
- Mitacs Accelerate Program
- Natural Sciences and Engineering Research Council of Canada
- British Columbia Knowledge Development Fund
This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery, roller bearings, and gearboxes. Compared with traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. The present approach can be extended to fault diagnosis of other machinery with various types of sensors due to its end to end feature learning capability.
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