Magnetic Flux Leakage Signal Inversion Based on Improved Efficient Population Utilization Strategy for Particle Swarm Optimization
- Авторы: Han W.1, Wu Z.1, Zhou M.2, Hou E.2, Su X.1, Wang P.1, Tian G.3
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Учреждения:
- College of Automation Engineering
- Department of Electrical and Computer Engineering
- School of Electrical and Electronic Engineering
- Выпуск: Том 53, № 12 (2017)
- Страницы: 862-873
- Раздел: Magnetic Methods
- URL: https://journals.rcsi.science/1061-8309/article/view/181501
- DOI: https://doi.org/10.1134/S1061830917120075
- ID: 181501
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Аннотация
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.
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Об авторах
Wenhua Han
College of Automation Engineering
Автор, ответственный за переписку.
Email: hanwenhua@shiep.edu.cn
Китай, Shanghai, 200090
Zhengyang Wu
College of Automation Engineering
Email: hanwenhua@shiep.edu.cn
Китай, Shanghai, 200090
Mengchu Zhou
Department of Electrical and Computer Engineering
Email: hanwenhua@shiep.edu.cn
США, Newark, NJ, 07102
Edwin Hou
Department of Electrical and Computer Engineering
Email: hanwenhua@shiep.edu.cn
США, Newark, NJ, 07102
Xiaoyan Su
College of Automation Engineering
Email: hanwenhua@shiep.edu.cn
Китай, Shanghai, 200090
Ping Wang
College of Automation Engineering
Email: hanwenhua@shiep.edu.cn
Китай, Nanjing, 210016
Guiyun Tian
School of Electrical and Electronic Engineering
Email: hanwenhua@shiep.edu.cn
Великобритания, Newcastle upon Tyne, NE1 7RU
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