Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT

This paper studies the defect detection problem of adhesive layer of thermal insulation materials. A novel detection method based on an improved particle swarm optimization (PSO) algorithm of Electrical Capacitance Tomography (ECT) is presented. Firstly, a least squares support vector machine is app...

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Main Authors:	Yintang Wen, Yao Jia, Yuyan Zhang, Xiaoyuan Luo, Hongrui Wang
Format:	Article
Language:	English
Published:	MDPI AG 2017-10-01
Series:	Sensors
Subjects:	thermal insulation material electrical capacitance tomography defect detection image reconstruction PSO
Online Access:	https://www.mdpi.com/1424-8220/17/11/2440

id	doaj-e7285fffe9f84d2ea741e07c58ff99d7
record_format	Article
spelling	doaj-e7285fffe9f84d2ea741e07c58ff99d72020-11-25T00:38:54ZengMDPI AGSensors1424-82202017-10-011711244010.3390/s17112440s17112440Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECTYintang Wen0Yao Jia1Yuyan Zhang2Xiaoyuan Luo3Hongrui Wang4School of Science and Technology, Yanshan University, Qinhuangdao 066004, ChinaSchool of Electrical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Electrical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Electrical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Electrical Engineering, Yanshan University, Qinhuangdao 066004, ChinaThis paper studies the defect detection problem of adhesive layer of thermal insulation materials. A novel detection method based on an improved particle swarm optimization (PSO) algorithm of Electrical Capacitance Tomography (ECT) is presented. Firstly, a least squares support vector machine is applied for data processing of measured capacitance values. Then, the improved PSO algorithm is proposed and applied for image reconstruction. Finally, some experiments are provided to verify the effectiveness of the proposed method in defect detection for adhesive layer of thermal insulation materials. The performance comparisons demonstrate that the proposed method has higher precision by comparing with traditional ECT algorithms.https://www.mdpi.com/1424-8220/17/11/2440thermal insulation materialelectrical capacitance tomographydefect detectionimage reconstructionPSO
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yintang Wen Yao Jia Yuyan Zhang Xiaoyuan Luo Hongrui Wang
spellingShingle	Yintang Wen Yao Jia Yuyan Zhang Xiaoyuan Luo Hongrui Wang Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT Sensors thermal insulation material electrical capacitance tomography defect detection image reconstruction PSO
author_facet	Yintang Wen Yao Jia Yuyan Zhang Xiaoyuan Luo Hongrui Wang
author_sort	Yintang Wen
title	Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT
title_short	Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT
title_full	Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT
title_fullStr	Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT
title_full_unstemmed	Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT
title_sort	defect detection of adhesive layer of thermal insulation materials based on improved particle swarm optimization of ect
publisher	MDPI AG
series	Sensors
issn	1424-8220
publishDate	2017-10-01
description	This paper studies the defect detection problem of adhesive layer of thermal insulation materials. A novel detection method based on an improved particle swarm optimization (PSO) algorithm of Electrical Capacitance Tomography (ECT) is presented. Firstly, a least squares support vector machine is applied for data processing of measured capacitance values. Then, the improved PSO algorithm is proposed and applied for image reconstruction. Finally, some experiments are provided to verify the effectiveness of the proposed method in defect detection for adhesive layer of thermal insulation materials. The performance comparisons demonstrate that the proposed method has higher precision by comparing with traditional ECT algorithms.
topic	thermal insulation material electrical capacitance tomography defect detection image reconstruction PSO
url	https://www.mdpi.com/1424-8220/17/11/2440
work_keys_str_mv	AT yintangwen defectdetectionofadhesivelayerofthermalinsulationmaterialsbasedonimprovedparticleswarmoptimizationofect AT yaojia defectdetectionofadhesivelayerofthermalinsulationmaterialsbasedonimprovedparticleswarmoptimizationofect AT yuyanzhang defectdetectionofadhesivelayerofthermalinsulationmaterialsbasedonimprovedparticleswarmoptimizationofect AT xiaoyuanluo defectdetectionofadhesivelayerofthermalinsulationmaterialsbasedonimprovedparticleswarmoptimizationofect AT hongruiwang defectdetectionofadhesivelayerofthermalinsulationmaterialsbasedonimprovedparticleswarmoptimizationofect
_version_	1725295887448539136

Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT

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