摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的最新研究结果已经发表。据新华社西安新闻报道，“由于工业机器人的动态负荷持续较高，旋转矢量(RV)减速器更容易磨损和点蚀，并会因故障而产生异常振动和声音。”许多有经验的专家可以根据机器听觉对某些特殊声音的敏感度来判断机器的健康状况。本研究经费来源于国家重点研究开发项目。记者引用了西安交通大学的一项研究，“为了模拟人的听觉，可视化专家在R-V减速器运行时听到的声音，设计了MEL滤波器组，并引入了MEL谱图，以显示R-V减速器在不同工况下的声能分布，并构造了拼接特征来描述能量分布的纹理和边界。”为了准确识别RV减速器的健康状态，提出了一种融合声音和振动拼接特征的声-振融合方法，通过单关节RV减速器实验获得了不同健康状态下的声-振数据，50次运行的识别结果表明，该方法的平均准确率为93.83%，明显优于传统方法。
Abstract
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."
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