Hidden Markov model based rotate vector reducer fault detection using acoustic emissions

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 工业控制网络与系统研究室

	Hidden Markov model based rotate vector reducer fault detection using acoustic emissions
	An HB(安海博)1,4,5,6; Liang W(梁炜)1,5,6; Zhang YL(张吟龙)3; Tan JD(谈金东)2
刊名	INTERNATIONAL JOURNAL OF SENSOR NETWORKS
	2020
卷号	32 期号:2 页码:116-125
关键词	RV rotate vector reducer fault detection HMM hidden Markov model AE acoustic emission
ISSN号	1748-1279
产权排序	1
英文摘要	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), faults from RV reducer are 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 the subsequent series of observations within posteriori probability framework. Through this way, the random and unknown disturbance could be suppressed. Besides, HMM is also applied to separate AE signal bulks within one cycle that has 39 subcycles. 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 the effectiveness and accuracy of our RV reducer fault detection model.
资助项目	National Key Research and Development Program of China[2017YFE0101200] ; National Key Research and Development Program of China[2017YFE0123000] ; Science and Technology Planning Project of Guangdong Province[2019B090916001] ; National Natural Science Foundation of China[71661147005] ; National Natural Science Foundation of China[61903357] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20180020] ; Liaoning Provincial Natural Science Foundation of China[2019-YQ-09] ; Pearl River Nova Program of Guangzhou[201605131121390]
WOS关键词	NEURAL-NETWORK ; DECOMPOSITION ; CLASSIFICATION ; DIAGNOSIS
WOS研究方向	Computer Science ; Telecommunications
语种	英语
WOS记录号	WOS:000513501100006
资助机构	National Key Research and Development Program of China [2017YFE0101200, 2017YFE0123000] ; Science and Technology Planning Project of Guangdong Province [2019B090916001] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [71661147005, 61903357] ; International Partnership Program of Chinese Academy of Sciences [173321KYSB20180020] ; Liaoning Provincial Natural Science Foundation of China [2019-YQ-09] ; Pearl River Nova Program of Guangzhou [201605131121390]
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/26300]
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	Liang W(梁炜)
作者单位	1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China 2.Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Konxville, TN, 37996, USA 3.Shenyang Institute of Automation, Guangzhou, Chinese Academy of Sciences, Guangzhou, 511548, China 4.University of Chinese Academy of Sciences, Beijing, 100049, China 5.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 6.State Key Laboratory of Robotics, Chinese Academy of Sciences, Shenyang, 110016, China
推荐引用方式 GB/T 7714	An HB,Liang W,Zhang YL,et al. Hidden Markov model based rotate vector reducer fault detection using acoustic emissions[J]. INTERNATIONAL JOURNAL OF SENSOR NETWORKS,2020,32(2):116-125.
APA	An HB,Liang W,Zhang YL,&Tan JD.(2020).Hidden Markov model based rotate vector reducer fault detection using acoustic emissions.INTERNATIONAL JOURNAL OF SENSOR NETWORKS,32(2),116-125.
MLA	An HB,et al."Hidden Markov model based rotate vector reducer fault detection using acoustic emissions".INTERNATIONAL JOURNAL OF SENSOR NETWORKS 32.2(2020):116-125.