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Feature Selection by Merging Sequential Bidirectional Search into Relevance Vector Machine in Condition Monitoring

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成果类型:
期刊论文
作者:
Zhang Kui*;Dong Yu;Ball, Andrew
通讯作者:
Zhang Kui
作者机构:
[Zhang Kui] Wuhan Polytech Univ, Sch Econ & Management, Wuhan 430023, Peoples R China.
[Dong Yu] Commun Univ China, Sch Informat Engn, Beijing 100024, Peoples R China.
[Ball, Andrew] Univ Huddersfield, Sch Comp & Engn, Huddersfield HD1 3DH, W Yorkshire, England.
通讯机构:
[Zhang Kui] W
Wuhan Polytech Univ, Sch Econ & Management, Wuhan 430023, Peoples R China.
语种:
英文
关键词:
feature selection;relevance vector machine;sequential bidirectional search;fault diagnosis
期刊:
中国机械工程学报
ISSN:
1000-9345
年:
2015
卷:
28
期:
6
页码:
1248-1253
基金类别:
Humanities and Social Science Programme in Hubei Province, China [14Y035]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71203170]; National Special Research Project in Food Nonprofit Industry [201413002-2]
机构署名:
本校为第一且通讯机构
院系归属:
经济学院
摘要:
For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of...

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