Quantitative Detection of Remanence in Broken Wire Rope Based on Adaptive Filtering and Elman Neural Network

doi:10.1007/s11668-019-00709-8

	Quantitative Detection of Remanence in Broken Wire Rope Based on Adaptive Filtering and Elman Neural Network
	Zhang, JuWei 1; Lu, ShiLiang 1; Gao, TianYi2
	2019
发表期刊	JOURNAL OF FAILURE ANALYSIS AND PREVENTION
ISSN	18641245
卷号	19 期号:5 页码:1264-1274
发表状态	已发表
DOI	10.1007/s11668-019-00709-8
摘要	In recent years, non-destructive testing methods for wire ropes based on remanence have attracted industry attention. The remanence detection methods have the characteristics of light equipment, high lifting value, high detection precision and low requirements on site conditions. An adaptive filtering algorithm based on wavelet decomposition was proposed to deal with the noise reduction of broken wire rope remanence data. The digital image processing method was used to locate and segment the defect. The texture features, morphological features and seventh-order invariant moments of the defect image were extracted as feature vectors, and an Elman neural network was designed to quantitatively identify the broken wires. The experimental results show that the designed filtering algorithm can effectively suppress the noise in the original signal, and the Elman recognition network has better performance of broken wire recognition. © 2019, ASM International.
收录类别	ESCI ; EI
语种	英语
资助项目	Science and Technology Department of Henan Province[17A510009] ; Science and Technology Department of Henan Province[182106000026] ; [152102210284] ; National Natural Science Foundation of China[61172014]
出版者	Springer New York LLC
EI入藏号	20193607409043
EI主题词	Adaptive filters ; Bridge decks ; Defects ; Image segmentation ; Nondestructive examination ; Remanence ; Textures ; Wavelet decomposition ; Wire rope
EI分类号	Bridges:401.1 ; Metal Forming:535.2 ; Magnetism: Basic Concepts and Phenomena:701.2 ; Mathematical Transformations:921.3 ; Materials Science:951
原始文献类型	Journal article (JA)
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/86825
专题	物质科学与技术学院_本科生物质科学与技术学院_硕士生
通讯作者	Zhang, JuWei; Lu, ShiLiang
作者单位	1.College of Electrical Engineering, Henan University of Science and Technology, Luoyang; 471023, China 2.School of Physical Science and Technology, ShanghaiTech University, Shanghai; 201210, China
推荐引用方式 GB/T 7714	Zhang, JuWei,Lu, ShiLiang,Gao, TianYi. Quantitative Detection of Remanence in Broken Wire Rope Based on Adaptive Filtering and Elman Neural Network[J]. JOURNAL OF FAILURE ANALYSIS AND PREVENTION,2019,19(5):1264-1274.
APA	Zhang, JuWei,Lu, ShiLiang,&Gao, TianYi.(2019).Quantitative Detection of Remanence in Broken Wire Rope Based on Adaptive Filtering and Elman Neural Network.JOURNAL OF FAILURE ANALYSIS AND PREVENTION,19(5),1264-1274.
MLA	Zhang, JuWei,et al."Quantitative Detection of Remanence in Broken Wire Rope Based on Adaptive Filtering and Elman Neural Network".JOURNAL OF FAILURE ANALYSIS AND PREVENTION 19.5(2019):1264-1274.