Industrial robot rotate vector reducer fault detection based on hidden markov models
文献类型:会议论文
作者 | Zhang YL(张吟龙)2,3,4![]() ![]() ![]() ![]() |
出版日期 | 2019 |
会议日期 | December 6-8, 2019 |
会议地点 | Dali, China |
页码 | 3013-3018 |
其他题名 | Industrial robot rotate vector reducer fault detection based on hidden markov models.pdf |
英文摘要 | Reliable fault detection of rotate vector (RV) reducer is of paramount importance for the long-term maintenance of high-precision industrial robots. This paper proposes a Hidden Markov Model (HMM) based RV reducer fault detection using Acoustic Emission (AE) measurements. Compared with the conventional faults from the common rotating machinery (such as bearings and gears), the fault from the RV reducer is more complicated and undetectable due to its inherent inline and two-stage meshing structure. To this end, this work modifies the HMM model by taking into account not only the current observations and previous states, but also the subsequent series of observations within the posteriori probability framework. Through this way, the random and unknown disturbance, which is common in the industrial scenarios, could be reduced. Besides, the HMM is also applied to separate the AE signal bulks within one cycle that has 39 subcycles, which is a critical step for AE signal pre-processings. The proposed method has been evaluated on our collected AE signal dataset from the RV reducer in the industrial robotic platform. The experimental results and analysis validate that the proposed HMM based RV Reducer fault detection model can reliably and accurately detect reducer faults. |
产权排序 | 1 |
会议录 | IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
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会议录出版者 | IEEE |
会议录出版地 | New York |
语种 | 英语 |
ISBN号 | 978-1-7281-6321-5 |
源URL | [http://ir.sia.cn/handle/173321/26283] ![]() |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | An HB(安海博); Liang W(梁炜) |
作者单位 | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.Shenyang Institute of Automation, Guangzhou, Chinese Academy of Sciences, Guangzhou 511548, China 3.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 5.University of Chinese Academy of Sciences, Beijing 100049, China 6.Fifth Electronic Instituteof MIIT, Guangzhou 510610, China 7.Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Konxville, TN 37996, United States |
推荐引用方式 GB/T 7714 | Zhang YL,An HB,Ding XJ,et al. Industrial robot rotate vector reducer fault detection based on hidden markov models[C]. 见:. Dali, China. December 6-8, 2019. |
入库方式: OAI收割
来源:沈阳自动化研究所
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