首页|New Findings Reported from Huazhong University of Science and Technology Describ e Advances in Robotics (Mjar: a Novel Joint Generalization-based Diagnosis Metho d for Industrial Robots With Compound Faults)
New Findings Reported from Huazhong University of Science and Technology Describ e Advances in Robotics (Mjar: a Novel Joint Generalization-based Diagnosis Metho d for Industrial Robots With Compound Faults)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Robotic s. According to news reporting originating from Wuhan,People's Republic of Chin a,by NewsRx correspondents,research stated,"Compound faults inevitably occur in multi-joint industrial robots resulting in excessive vibration. Intelligent d iagnosis for the occurrence and position of fault joints can efficiently reduce the maintenance cost." Financial support for this research came from Basic and Applied Basic Research F und of Guangdong Province. Our news editors obtained a quote from the research from the Huazhong University of Science and Technology,"Compared with laboratory disassembly parts,joint d iagnosis of in-situ industrial robots is more challenging due to coupling of mul tiple subsystems and the industrial noise interference. This paper proposes a no vel joint generalization-based fault diagnosis method for industrial robots via the multi-joint attention residual network (MJAR) model. Each joint signals are independently input to parallel residual convolution with unidirectional matrix kernel (ResCUM) in multi-joint decoupling attention (MJDA) module,which is excl usively designed combining residual and attention mechanism to provide decouplin g capability of compound faults. And the multi-spatial reconstruction (MSR) modu le based on sparse sampling is design to provide multi-scale feature extraction adapted to real industrial signals. MJAR can diagnose unseen compound fault comb inations without using any transfer strategy as the robot joint generalization-b ased fault diagnosis (RJGFD) framework."
WuhanPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRoboticsHuazhong Univers ity of Science and Technology
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