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Microscopic Machine Vision Based Degradation Monitoring of Low-Voltage Electromagnetic Coil Insulation Using Ensemble Learning in a Membrane Computing Framework
Li C(李晨)1; Kong FJ(孔樊杰)1,2; Wang K(王锴)3; Xu AD(徐皑冬)3; Zhang, Gexiang4; Xu, Ning5; Liu ZH(刘志华)3; Guo HF(郭海丰)3; Wang, Xue1; Liang, Kuan1; Yuan, Jianying4,6; Qi, Shouliang1; Jiang T(蒋涛)6
Department工业控制网络与系统研究室
Source PublicationIEEE ACCESS
ISSN2169-3536
2019
Volume7Pages:97216-97241
Indexed BySCI ; EI
EI Accession number20193207296730
WOS IDWOS:000478964800002
Contribution Rank3
Funding OrganizationNational 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
KeywordLow-voltage electromagnetic coil insulation degradation monitoring ensemble learning machine vision membrane computing microscopic image analysis feature extraction
AbstractIn 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.
Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS KeywordSYSTEMS ; CLASSIFICATION ; CONTROLLERS ; DIAGNOSIS ; FEATURES ; MOTORS ; IMAGES ; AGE
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Funding ProjectNational 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]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25462
Collection工业控制网络与系统研究室
Corresponding AuthorWang K(王锴); Jiang T(蒋涛)
Affiliation1.Microscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang 110819, China
2.Pratt School of Engineering, Duke University, Durham, NC 27708, USA
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
4.School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
5.School of Art and Design, Liaoning Shihua University, Fushun 113001, China
6.Control Engineering College, Chengdu University of Information Technology, Chengdu 610103, China
Recommended Citation
GB/T 7714
Li C,Kong FJ,Wang K,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 Li C.,Kong FJ.,Wang K.,Xu AD.,Zhang, Gexiang.,...&Jiang T.(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 Li C,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.
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