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Fault diagnosis of pump based on a hybrid HMM/SVM model

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成果类型:
期刊论文、会议论文
作者:
Yue, X.*;Zhang, C. L.;Li, J.;Zhu, H. Y.
通讯作者:
Yue, X.
作者机构:
[Yue, X.] S China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Guangdong, Peoples R China.
[Zhang, C. L.; Zhu, H. Y.] Guangzhou Univ, Sch Mech & Elect Engineer, Guangzhou, Peoples R China.
[Li, J.] Univ South China, Sch Mech Engn, Hengyang, Peoples R China.
通讯机构:
[Yue, X.] S
S China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Guangdong, Peoples R China.
语种:
英文
关键词:
Fault diagnosis;Pump;HMM;SVM;Hybrid model
期刊:
Advanced Materials Research
ISSN:
1022-6680
年:
2011
卷:
188
页码:
629-635
会议名称:
The 4th International Conference on High Speed Machining(第四届高速加工国际会议 ICHSM'2010)
会议论文集名称:
The 4th International Conference on High Speed Machining(第四届高速加工国际会议 ICHSM'2010)论文集
会议时间:
2011-05-28
会议地点:
武汉
会议主办单位:
[Yue, X.] S China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Guangdong, Peoples R China.^[Zhang, C. L.;Zhu, H. Y.] Guangzhou Univ, Sch Mech & Elect Engineer, Guangzhou, Peoples R China.^[Li, J.] Univ South China, Sch Mech Engn, Hengyang, Peoples R China.
会议赞助商:
中国地质大学<&wdkj&>电子商务国际合作中心
主编:
Wang, CY He, N Chen, M Huang, CZ
出版地:
KREUZSTRASSE 10, 8635 DURNTEN-ZURICH, SWITZERLAND
出版者:
TRANS TECH PUBLICATIONS LTD
ISBN:
978-3-03785-067-1
基金类别:
National Defense Basic Scientific Research Project of China [B0120060585]; National Hi-tech Research and Development Program of ChinaNational High Technology Research and Development Program of China [2008AA04Z407]
机构署名:
本校为其他机构
院系归属:
机械工程学院
摘要:
A hybrid support vector machine (SVM) and hidden Markov model (HMM) model was introduced into the fault diagnosis of pump. This model had double layers: the first layer used HMM to classify preliminarily in order to get the coverage of possible faults; the second layer utilized this information to activate the corresponding SVMs for improving the recognition accuracy. The structure of this hybrid model was clear and feasible. Especially the model had the potential of large-scale multiclass application in fault diagnosis because of its good scalability. The recognition experiments of 26 statuse...

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