A novel RSG-based intelligent bearing fault diagnosis method for motors in high-noise industrial environment |
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| Affiliation: | 1. School of Electronic Information Engineering, Shanghai Dianji University, 300 Shuihua Road, Shanghai 201306, China;2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China;3. School of Higher Vocational Technology, Shanghai Dianji University, 300 Shuihua Road, Shanghai 201306, China;4. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China;5. College of Engineering and Physical Sciences, Aston University, Birmingham B47ET, UK;1. School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, PR China;2. Department of Mechanical Engineering, National University of Singapore, 119077, Singapore;1. National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, Jiangsu, China;2. International Shipping Research Institute, GongQing Institute of Science and Technology, Jiujiang 332020, China;3. Institute of Advanced Manufacturing and Modern Equipment Technology, Jiangsu University, Zhenjiang 212013, China;4. Ningbo Academy of Product and Food Quality Inspection, Ningbo 315048, Zhejiang, China;1. School of Mechanical Engineering, North University of China, Taiyuan, Shanxi 030051, China;2. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, China |
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| Abstract: | Bearing fault diagnosis is a critical and challenging task for prognostics and health management of motors. The ability to efficiently and accurately classify the fault categories based on sensor signals is the key to successful bearing fault diagnosis. Although various data-driven methods have been developed for fault diagnosis in recent years, automatic and effective extraction of discriminative fault features from high-noise vibration signals generated in the real-world industrial environment remains a challenging task. To tackle this challenge, this paper proposes a novel deep learning method based on the combination of residual building Unit, soft thresholding and global context, called RSG, to solve the complex mapping relationship between vibration signals and different types of bearing faults. The proposed RSG integrates the working mechanisms of soft threshold and global context to achieve effective noise reduction and feature extraction. A comparative analysis is performed to demonstrate the advantages of the proposed method. Furthermore, the proposed method is tested on a faulty motor dataset collected by our developed intelligent motor test platform based on Industrial Internet of Things. Experimental results show that our method can achieve an average fault diagnosis accuracy of 98%. Thus, the proposed method proves to be an efficient solution for intelligent bearing fault diagnosis for motors in a high-noise industrial environment. |
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| Keywords: | Fault diagnosis Deep residual unit Soft thresholds Global context Deep learning |
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