Rolling element bearing fault diagnosis using convolutional neural network and vibration image |
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| Affiliation: | 1. Graduate School of Electrical Engineering, University of Ulsan, Ulsan, South Korea;2. School of Electrical Engineering, University of Ulsan, Ulsan, South Korea;1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China;2. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China;1. School of Mechanical Engineering, Tianjin University, Tianjin 300354, China;2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi''an 710049, China;3. NSF/UCRC Center for Intelligent Maintenance Systems, University of Cincinnati, OH 45221, USA |
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| Abstract: | Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network. Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments. |
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| Keywords: | Bearing fault diagnosis Convolutional neural network Deep learning Machine learning |
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