Microscopic Machine Vision Based Degradation Monitoring of Low-Voltage Electromagnetic Coil Insulation Using Ensemble Learning in a Membrane Computing Framework

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	Microscopic Machine Vision Based Degradation Monitoring of Low-Voltage Electromagnetic Coil Insulation Using Ensemble Learning in a Membrane Computing Framework
	Jiang T(蒋涛)1; Li C(李晨)6; Kong FJ(孔樊杰)5,6; Wang K(王锴)4 ; Xu AD(徐皑冬)4 ; Zhang, Gexiang 3; Xu, Ning 2; Liu ZH(刘志华)4 ; Guo HF(郭海丰)4 ; Wang, Xue 6
刊名	IEEE ACCESS
	2019
卷号	7 页码:97216-97241
关键词	Low-voltage electromagnetic coil insulation degradation monitoring ensemble learning machine vision membrane computing microscopic image analysis feature extraction
ISSN号	2169-3536
产权排序	3
英文摘要	In this paper, a novel microscopic machine vision system is proposed to solve a degradation monitoring problem of low-voltage electromagnetic coil insulation in practical industrial fields, where an ensemble learning approach in a compound membrane computing framework is newly introduced. This membrane computing framework is constituted by eight layers, 29 membranes, 72 objects, and 35 rules. In this framework, multiple machine learning methods, including classical pattern recognition methods and novel deep learning methods, are tested and compared. First, the most optimal feature extraction approaches are selected. Then, the selected approaches are fused together to achieve an even better monitoring performance. Third, a large number of experiments are used to evaluate and prove the usefulness and potential of the proposed system, where a mean accuracy of 61.4% is achieved on 1035 validation images of six degradation states with single state matching, and mean accuracies of 61.0% and 77.4% are achieved on 622 test images of six degradation states with single state matching and state range matching, respectively. Finally, a mechanical device is designed to apply the system to real industrial tasks.
资助项目	National Natural Science Foundation of China[61806047] ; Fundamental Research Funds for the Central Universities[N171903004] ; Scientific Research Launched Fund of Liaoning Shihua University[2017XJJ-061] ; Sichuan Science and Technology Program China[2018GZ0385]
WOS关键词	SYSTEMS ; CLASSIFICATION ; CONTROLLERS ; DIAGNOSIS ; FEATURES ; MOTORS ; IMAGES ; AGE
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
WOS记录号	WOS:000478964800002
资助机构	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Scientific Research Launched Fund of Liaoning Shihua University ; Sichuan Science and Technology Program China
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/25462]
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	Jiang T(蒋涛); Wang K(王锴)
作者单位	1.Control Engineering College, Chengdu University of Information Technology, Chengdu 610103, China 2.School of Art and Design, Liaoning Shihua University, Fushun 113001, China 3.School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China 4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 5.Pratt School of Engineering, Duke University, Durham, NC 27708, USA 6.Microscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang 110819, China
推荐引用方式 GB/T 7714	Jiang T,Li C,Kong FJ,et al. Microscopic Machine Vision Based Degradation Monitoring of Low-Voltage Electromagnetic Coil Insulation Using Ensemble Learning in a Membrane Computing Framework[J]. IEEE ACCESS,2019,7:97216-97241.
APA	Jiang T.,Li C.,Kong FJ.,Wang K.,Xu AD.,...&Yuan, Jianying.(2019).Microscopic Machine Vision Based Degradation Monitoring of Low-Voltage Electromagnetic Coil Insulation Using Ensemble Learning in a Membrane Computing Framework.IEEE ACCESS,7,97216-97241.
MLA	Jiang T,et al."Microscopic Machine Vision Based Degradation Monitoring of Low-Voltage Electromagnetic Coil Insulation Using Ensemble Learning in a Membrane Computing Framework".IEEE ACCESS 7(2019):97216-97241.