Generative adversarial one-shot diagnosis of transmission faults for industrial robots |
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| Affiliation: | 1. Xidian University, Xi''an, China;2. KTH Royal Institute of Technology, Stockholm, Sweden;3. University of Patras, Patras, Greece;4. Technical University of Berlin, Berlin, Federal Republic of Germany;1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China;3. School of Mechanical and Electrical Engineering, Shenyang Aerospace University, Shenyang 110136, China;1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China;2. State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China;1. Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China;2. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;3. Department of Production Engineering, KTH Royal Institute of Technology, 11428 Stockholm, Sweden;1. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China;2. Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China;3. Hubei Longzhong Laboratory, Xiangyang 441000, China |
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| Abstract: | Transmission systems of industrial robots are prone to get failures due to harsh operating environments. Fault diagnosis is of great significance for realizing safe operations for industrial robots. However, it is difficult to obtain faulty data in real applications. To migrate this issue, a generative adversarial one-shot diagnosis (GAOSD) approach is proposed to diagnose robot transmission faults with only one sample per faulty pattern. Signals representing kinematical characteristics were acquired by an attitude sensor. A bidirectional generative adversarial network (Bi-GAN) was then trained using healthy signals. Inspired by way of human thinking, the trained encoder in Bi-GAN was taken out to perform information abstraction for all signals. Finally, the abstracted signals were sent to a random forest for the one-shot diagnosis. The performance of the present technique was evaluated on an industrial robot experimental setup. Experimental results show that the proposed GAOSD has promising performance on the fault diagnosis of robot transmission systems. |
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| Keywords: | One-shot diagnosis Bi-directional generative adversarial network Random forest Industrial robot Transmission system |
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