首页|Findings from Guangdong University of Technology Advance Knowledge in Robotics ( Transfer learning based cross-process fault diagnosis of industrial robots)
Findings from Guangdong University of Technology Advance Knowledge in Robotics ( Transfer learning based cross-process fault diagnosis of industrial robots)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - Fresh data on robotics are presented i n a new report. According to news originating from Guangzhou, People’s Republic of China, by NewsRx editors, the research stated, “In the actual industrial appl ication of robots, the characteristics of robot malfunctions change accordingly as the working environment becomes increasingly diverse and complex.” The news reporters obtained a quote from the research from Guangdong University of Technology: “Utilizing the original fault diagnosis models in new working env ironments correspondingly leads to a decline in the performance and the generali zation capability of the model. Moreover, the monitoring data collected in new w orking processes often has limited or no labels, making the diagnosis models tra ined with this data unable to identify faults accurately. In this paper, we prop ose a Domain adaptive Cross-process Fault Diagnosis method (DCFD) to leverage kn owledge from existing working processes for diagnosing faults in new working pro cesses.”
Guangdong University of TechnologyGuan gzhouPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRobotics
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