首页|Researchers' Work from Xi'an Jiaotong University Focuses on Robotics (Sound-vibr ation Spectrogram Fusion Method for Diagnosis of Rv Reducers In Industrial Robot s)
Researchers' Work from Xi'an Jiaotong University Focuses on Robotics (Sound-vibr ation Spectrogram Fusion Method for Diagnosis of Rv Reducers In Industrial Robot s)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news reporting originating in Xi'an, People's Repu blic of China, by NewsRx journalists, research stated, "Due to continuous high d ynamic load of industrial robots, rotate vector (RV) reducers may be more prone to wear and pitting and there will be abnormal vibration and sound caused by inc ipient faults. However, many experienced experts can identify the health state o f machines by the sensitivity of their hearing to some special sounds." Financial support for this research came from National Key Research and Developm ent Program of China. The news reporters obtained a quote from the research from Xi'an Jiaotong Univer sity, "To simulate human hearing and visualize the sound heard by experts when R V reducer is running, Mel-filter bank is designed and Melspectrograms are introd uced to show the sound energy distribution of RV reducers under various conditio ns. A spliced feature is constructed to describe the texture and boundary of the energy distribution. Moreover, a sound-vibration fusion approach is proposed to fuse the spliced features of sound and vibration for accurately identifying of RV reducer health states. Single-joint RV reducers experiments are performed to obtain sound and vibration data sets under different health states. The identifi cation results of 50 runs show that the average accuracy of the proposed sound-v ibration spectrogram fusion method is 93.83 %, greatly preferable t o traditional methods."
Xi'anPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRoboticsXi'an Jiaotong U niversity
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