首页|Recent Findings from Xi’an Jiaotong University Provides New Insights into Robotics (Nonlinear Spectrum Feature Fusion Diagnosis Method for Rv Reducer of Industrial Robots)
Recent Findings from Xi’an Jiaotong University Provides New Insights into Robotics (Nonlinear Spectrum Feature Fusion Diagnosis Method for Rv Reducer of Industrial Robots)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
Investigators publish new report on Robotics. According to news reporting originating from Xi’an, People’s Republic of China, by NewsRx correspondents, research stated, “RV reducers of industrial robots may be possibly subjected to wear and vibration impact and invalidated for keeping the repeatability due to long-time dynamic load in their life cycle. Any failure of RV reducers may decrease the reliability and lead to incredible loss due to the unexpectedly shutdown of the manufacturing system.” Financial support for this research came from National Key Research and Development Program of China. Our news editors obtained a quote from the research from Xi’an Jiaotong University, “Failures can cause nonlinear interference when occurring in RV reducers and pose a challenge to RV reducer fault diagnosis. Therefore, it is important to take proper measures to diagnose the failure in RV reducers and reduce the influence of nonlinear interference. Nonlinear spectrum features fusion method is proposed for diagnosing the faults of RV reducers in industrial robots. For the purpose of extracting fault features more effectively, nonlinear output frequency response function is employed to obtain nonlinear frequency spectrum for exactly describing the nonlinear mechanism of faulty phenomenon. Moreover, enhanced evidence theory with similarity measure is proposed to compute the basic probability assignment functions of evidences according to the similarity between different modes for improving the identification accuracy of different faults. RV reducer experiments with different faults are performed to simulate robot operating conditions for obtaining normal and fault data under a variable speed working condition. Among the 180 sets of test data including normal conditions, the diagnostic accuracy of the proposed nonlinear feature fusion method is 92.22% and greatly preferable to that of the ordinary frequency spectrum method.”
Xi’anPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRoboticsXi’an Jiaotong University
NETL
NSTL
Elsevier
