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wozhucel

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[求助] 帮忙查询一篇期刊论文的SCI检索号

Gang Yin, Yingtang Zhang, et al. Online fault diagnosis method based on incremental support vector data description and extreme learning machine with incremental output structure, neurocomputing, 128(2014)224-231.

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oven1986: 检索EPI+1, 感谢应助，鼓励一下。 2014-04-03 09:53:14

Online fault diagnosis method based on Incremental Support Vector Data Description and Extreme Learning Machine with incremental output structure

作者:Yin, G (Yin, Gang)[ 1 ] ; Zhang, YT (Zhang, Ying-Tang)[ 1 ] ; Li, ZN (Li, Zhi-Ning)[ 1 ] ; Ren, GQ (Ren, Guo-Quan)[ 1 ] ; Fan, HB (Fan, Hong-Bo)[ 1 ]

NEUROCOMPUTING

卷: 128

页: 224-231

DOI: 10.1016/j.neucom.2013.01.061

出版年: MAR 27 2014

查看期刊信息



会议名称

会议: International Workshop of Extreme Learning Machines (ELM)

会议地点: Singapore, SINGAPORE

会议日期: DEC 11-13, 2012

摘要

Online fault diagnosis system should be able to detect faults, recognize fault types and update the discriminating ability and knowledge of itself automatically in real time. But the class number in fault diagnosis is not constant and it is in a dynamic state with new members enrolled. The traditional recognition algorithms are not able to update diagnosis system efficiently when the class number of failure modes is increasing. To solve the problem, an online fault diagnosis method based on Incremental Support Vector Data Description (ISVDD) and Extreme Learning Machine with incremental output structure (IOELM) is proposed. ISVDD is used to find a new failure mode quickly in the continuous condition monitoring of the equipments. The fixed structure of Extreme Learning Machine is changed into an elastic structure whose output nodes could be added incrementally to recognize the new fault mode efficiently. Recognition experiments on the diesel engine under eleven different conditions show that the online fault diagnosis method based on ISVDD and IOELM works well, and the method is also feasible in fault diagnosis of other mechanical equipments. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

关键词

作者关键词:Incremental Support Vector Data; Description; Extreme Learning Machine; Multi-scale principal component analysis; Online fault diagnosis

KeyWords Plus:QUANTITATIVE MODEL

作者信息

通讯作者地址: Yin, G (通讯作者)

   Mech Engn Coll, Dept 7, Shijiazhuang, Peoples R China.

地址:

   [ 1 ] Mech Engn Coll, Dept 7, Shijiazhuang, Peoples R China

电子邮件地址:gang.gang88@163.com

出版商

ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS

类别 / 分类

研究方向:Computer Science

Web of Science 类别:Computer Science, Artificial Intelligence

文献信息

文献类型:Article; Proceedings Paper

语种:English

入藏号: WOS:000331851700027

ISSN: 0925-2312

电子 ISSN: 1872-8286