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【答案】应助回帖
★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★
wozhucel: 金币+20, ★★★★★最佳答案 2014-04-03 08:57:58
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 |
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