Alternative multi-label imitation learning framework monitoring tool wear and bearing fault under different working conditions |
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| Affiliation: | 1. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore;3. Department of Mechanical and Electromechanical Engineering, National ILan University, ILan 26041, Taiwan;4. Huazhong University of Science and Technology – Wuxi Research Institute, Wuxi 214000, China;5. School of Mechanical Engineering, Hubei University of Technology, Wuhan 430072, China;1. School of Economics and Management, Beihang University, Beijing 100191, China;2. Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education, Beijing 100191, China;3. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China;1. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Department of Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037, Luoyu Road, Hongshan District, Wuhan, Hubei 430074, China |
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| Abstract: | Bearings and tools are the important parts of the machine tool. And monitoring automatically the fault of bearings and the wear of tools under different working conditions is the necessary performance of the intelligent manufacturing system. In this paper, a multi-label imitation learning (MLIL) framework is proposed to monitor the tool wear and bearing fault under different working conditions. Specially, the multi-label samples with multiple sublabels are transformed into the imitation objects, and the MLIL develops a discriminator and a deep reinforcement learning (DRL) to imitate the feature from imitation objects. In detail, the DRL is implemented without setting the reward function to enhance the feature extraction ability of deep neural networks, and meanwhile the discriminator is used to discriminate the generations of DRL and imitation objects. As a result, the MLIL framework can not only deal with the correlation between multiple working conditions including different speeds and loads, but also distinguish the compound fault composed of coinstantaneous bearing fault and tool wear. Two cases demonstrate jointly the imitation ability of the MLIL framework on monitoring tool wear and bearing fault under different working conditions. |
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| Keywords: | Tool wear monitoring Bearing fault monitoring Multi-label learning Imitation learning Deep reinforcement learning |
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