期刊
COMPUTERS & CHEMICAL ENGINEERING
卷 49, 期 -, 页码 105-113出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2012.10.008
关键词
Fault diagnosis; Multi-fault classification; Feature extraction; Local structure preserving; Kernel methods
资金
- 973 Program of China [2012CB720500]
- National High Technology R&D Program of China [2012AA041102]
Linear Discriminant Analysis (LDA) and its nonlinear kernel variation Generalized Discriminant Analysis (GDA) are the most popular supervised dimensionality reduction methods for fault diagnosis. However, we argue that they probably provide suboptimal results for fault diagnosis due to the Fisher's criterion they use. This paper proposes a new supervised dimensionality reduction method named Locality Preserving Discriminant Analysis (LPDA) and its kernel variation Kernel LPDA (KLPDA) for fault diagnosis. (K) LPDA maximizes a new criterion such that local discriminant structure and local geometric structure in data are optimally preserved simultaneously in each dimension of the reduced space. The criterion directly targets at minimizing local overlapping between different classes. Extensive simulations on the Tennessee Eastman (TE) benchmark simulation process and a waste water treatment plant (WWTP) clearly demonstrate the superiority of our methods in terms of misclassification rate and making use of extra training data. (C) 2012 Elsevier Ltd. All rights reserved.
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