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Kernel Fisher discriminant anlysis for bearing fault diagnosis

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成果类型:
期刊论文
作者:
Zhang, JF*;Huang, ZC
通讯作者:
Zhang, JF
作者机构:
[Huang, ZC; Zhang, JF] Wuhan Polytech Univ, Dept Mech Engn, Wuhan 430023, Peoples R China.
通讯机构:
[Zhang, JF] W
Wuhan Polytech Univ, Dept Mech Engn, Wuhan 430023, Peoples R China.
语种:
英文
关键词:
kernel Fisher discriminant;condition monitoring;bearing faults;support vector machines
期刊:
Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9
年:
2005
页码:
3216-3220
机构署名:
本校为第一且通讯机构
院系归属:
机械工程学院
摘要:
A kernel Fisher discriminant (KFD) method is applied to the bearing fault diagnosis (i.e. classification of multiple fault classes). This paper deals with KFD for two multi-class fault recognition examples. One example is to recognize faults on different bearing elements; another is to recognize four different severities of the ball faults. The time-domain vibration signals of normal bearings, bearings with different faults have been used for feature extraction. The features are obtained from direct processing of the signal segments using simple preprocessing. The classification results demons...

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