Application of independent component analysis in machine fault diagnosis

doi:10.4028/www.scientific.net/AMR.905.524

CORC > 兰州理工大学 > 兰州理工大学

	Application of independent component analysis in machine fault diagnosis
	Miao, Feng 1,2; Zhao, Rong Zhen 2
	2014
会议日期	April 26, 2014 - April 27, 2014
会议地点	Hong Kong, China
关键词	Algorithms Blind source separation Electronics engineering Failure analysis Feature extraction Iterative methods Fixed-point algorithms Fixed-point iterations Independent component analysis(ICA) Machine fault diagnosis Neural network learning Noise elimination Redundancy reductions User-defined parameters
卷号	905
DOI	10.4028/www.scientific.net/AMR.905.524
页码	524-527
英文摘要	A novel fast algorithm for independent Component Analysis is introduced, which can be used for blind source separation and machine fault diagnosis feature extraction. It is shown how a neural network learning rule can be transformed into a fixed-point iteration, which provides an algorithm that is very simple, does not depend on any user-defined parameters, and is fast to converge to the most accurate solution allowed by the data. The purpose of this paper is to review the application of blind source separation in the machine fault diagnosis, including the following aspects: noise elimination and extraction of the weak signals, the separation of multi-fault sources, redundancy reduction, feature extraction and pattern classification based on independent component analysis. And its application in machine fault diagnosis is illustrated by the examples. In addition, some prospects about using blind source separation for machine fault diagnosis are discussed. © (2014) Trans Tech Publications, Switzerland.
会议录	Advanced Materials Research
会议录出版者	Trans Tech Publications
语种	英语
ISSN号	10226680
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/117792]
专题	兰州理工大学
作者单位	1.Gansu Institute of Metrology, Lanzhou, 730070, China 2.Institute of Electrical and Mechanical Engineering of Lanzhou University of Technology, Lanzhou, 730050, China;
推荐引用方式 GB/T 7714	Miao, Feng,Zhao, Rong Zhen. Application of independent component analysis in machine fault diagnosis[C]. 见:. Hong Kong, China. April 26, 2014 - April 27, 2014.