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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Bibliographic Details
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:
PSO
Online Access:https://www.mdpi.com/1424-8220/17/11/2440
Description
Summary: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.
ISSN:1424-8220