Magnetic Flux Leakage Signal Inversion Based on Improved Efficient Population Utilization Strategy for Particle Swarm Optimization
- Authors: Han W.1, Wu Z.1, Zhou M.2, Hou E.2, Su X.1, Wang P.1, Tian G.3
- 
							Affiliations: 
							- College of Automation Engineering
- Department of Electrical and Computer Engineering
- School of Electrical and Electronic Engineering
 
- Issue: Vol 53, No 12 (2017)
- Pages: 862-873
- Section: Magnetic Methods
- URL: https://journals.rcsi.science/1061-8309/article/view/181501
- DOI: https://doi.org/10.1134/S1061830917120075
- ID: 181501
Cite item
Abstract
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.
About the authors
Wenhua Han
College of Automation Engineering
							Author for correspondence.
							Email: hanwenhua@shiep.edu.cn
				                					                																			                												                	China, 							Shanghai, 200090						
Zhengyang Wu
College of Automation Engineering
														Email: hanwenhua@shiep.edu.cn
				                					                																			                												                	China, 							Shanghai, 200090						
Mengchu Zhou
Department of Electrical and Computer Engineering
														Email: hanwenhua@shiep.edu.cn
				                					                																			                												                	United States, 							Newark, NJ, 07102						
Edwin Hou
Department of Electrical and Computer Engineering
														Email: hanwenhua@shiep.edu.cn
				                					                																			                												                	United States, 							Newark, NJ, 07102						
Xiaoyan Su
College of Automation Engineering
														Email: hanwenhua@shiep.edu.cn
				                					                																			                												                	China, 							Shanghai, 200090						
Ping Wang
College of Automation Engineering
														Email: hanwenhua@shiep.edu.cn
				                					                																			                												                	China, 							Nanjing, 210016						
Guiyun Tian
School of Electrical and Electronic Engineering
														Email: hanwenhua@shiep.edu.cn
				                					                																			                												                	United Kingdom, 							Newcastle upon Tyne, NE1 7RU						
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