A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing

doi:10.1007/s10489-020-01684-6

	A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing
	Peng Chen; Xiaoqiang Zhao; Qixian Zhu
刊名	Applied Intelligence
	2020-07
卷号	50 期号:无页码:2833-2847
关键词	Chaotic systems Computer aided diagnosis Damage detection Failure analysis Fault detection Machine learning Optimization Extreme learning machine Fault detection and classification Global searching ability Heuristic optimization algorithms ICGOA- KELM Intrinsic Mode functions Optimization algorithms Rolling bearings
ISSN号	0924669X
DOI	10.1007/s10489-020-01684-6
英文摘要	A novel classification method based on ICGOA-KELM is presented in this paper. In ICGOA-KELM, an improved circle chaotic map with grasshopper optimization algorithm (ICGOA) is designed to optimize the parameters of Kernel extreme learning machine (KELM) to improve the stability and accuracy of fault classification for rolling bearing based on parameter modification of circle chaotic map. Grasshopper optimization algorithm (GOA) is a new heuristic optimization algorithm, which has strong global searching ability. However, it still may fall into local optimization in some cases. In this paper, the vibration signals of rolling bearing are preprocessed by using Variational Modal Decomposition (VMD). Then Multi-scale Permutation Entropy (MPE) is utilized to extracted features of intrinsic mode functions (IMFs) decomposed by VMD. In addition, KPCA is adopted to select the salient features with high contribution rates to remove redundant and irrelevant features. Finally, the salient features are fed into ICGOA-KELM to fulfill fault classification. Therefore, a new fault detection and classification method based on VMD, MPE, KPCA and ICGOA-KELM is proposed. This method is applied to the fault classification of rolling bearing and the identification of different damage fault degrees. Experiments verify that the proposed method is more effective than CGOA-KELM for fault diagnosis of rolling bearing. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
WOS研究方向	Computer Science
语种	英语
出版者	Springer
WOS记录号	WOS:000521881200001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/103246]
专题	电气工程与信息工程学院
推荐引用方式 GB/T 7714	Peng Chen,Xiaoqiang Zhao,Qixian Zhu. A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing[J]. Applied Intelligence,2020,50(无):2833-2847.
APA	Peng Chen,Xiaoqiang Zhao,&Qixian Zhu.(2020).A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing.Applied Intelligence,50(无),2833-2847.
MLA	Peng Chen,et al."A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing".Applied Intelligence 50.无(2020):2833-2847.