| 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
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| Department | 工业控制网络与系统研究室 |
| Source Publication | IEEE ACCESS
 |
| ISSN | 2169-3536 |
| 2019 | |
| Volume | 7Pages:97216-97241 |
| Indexed By | SCI ; EI |
| EI Accession number | 20193207296730 |
| WOS ID | WOS:000478964800002 |
| Contribution Rank | 3 |
| Funding Organization | 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 |
| Keyword | Low-voltage electromagnetic coil insulation degradation monitoring ensemble learning machine vision membrane computing microscopic image analysis feature extraction |
| Abstract | 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. |
| Language | 英语 |
| WOS Subject | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS Keyword | SYSTEMS ; CLASSIFICATION ; CONTROLLERS ; DIAGNOSIS ; FEATURES ; MOTORS ; IMAGES ; AGE |
| WOS Research Area | Computer Science ; Engineering ; Telecommunications |
| Funding Project | 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] |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.sia.cn/handle/173321/25462 |
| Collection | 工业控制网络与系统研究室 |
| Corresponding Author | Wang K(王锴); Jiang T(蒋涛) |
| Affiliation | 1.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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| Microscopic Machine （6392KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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