Magnetic Flux Leakage Signal Inversion Based on Improved Efficient Population Utilization Strategy for Particle Swarm Optimization


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In this paper, an improved efficient population utilization strategy for particle swarm optimization (IEPUS-PSO) for high dimension problem is proposed to estimate defect profile from magnetic flux leakage (MFL) signals. In the IEPUS-PSO, a mutation probability is proposed to distinguish local version and global version in particle change model and a self-adapted mutation operator, which is used to update the particles’ positions randomly, is introduced into EPUS-PSO. The IEPUS-PSO- based inversing technique is used to estimate the defect profiles. The estimated defect profiles of simulation signals demonstrate that the inversing technique based on the IEPUS-PSO outperforms the one based on EPUS-PSO. The results estimated from real MFL signals by the IEPUS-PSO-based inversing technique indicate that the algorithm is capable of decreasing the computation time. The results show that the IEPUS-PSO-based inversing technique could improve the reconstruction precision by two orders of magnitude for the MFL simulation signals, and for the real MFL signals, the computation time is reduced by about 30% nearly under the same reconstruction precision.

作者简介

Wenhua Han

College of Automation Engineering

编辑信件的主要联系方式.
Email: hanwenhua@shiep.edu.cn
中国, Shanghai, 200090

Zhengyang Wu

College of Automation Engineering

Email: hanwenhua@shiep.edu.cn
中国, Shanghai, 200090

Mengchu Zhou

Department of Electrical and Computer Engineering

Email: hanwenhua@shiep.edu.cn
美国, Newark, NJ, 07102

Edwin Hou

Department of Electrical and Computer Engineering

Email: hanwenhua@shiep.edu.cn
美国, Newark, NJ, 07102

Xiaoyan Su

College of Automation Engineering

Email: hanwenhua@shiep.edu.cn
中国, Shanghai, 200090

Ping Wang

College of Automation Engineering

Email: hanwenhua@shiep.edu.cn
中国, Nanjing, 210016

Guiyun Tian

School of Electrical and Electronic Engineering

Email: hanwenhua@shiep.edu.cn
英国, Newcastle upon Tyne, NE1 7RU

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