Abstract
Most of the existing fault detection techniques merely consider the global structure information, but neglect the local structure details between different data points. However, local structure information plays an essential role in data mining and feature extraction. In this article, a locality kernel canonical variate analysis (LPP-KCVA) method for fault detection is proposed. This method inherits the merits of KCVA in coping with nonlinear as well as dynamic problems, and makes full use of the advantage of Locality Preserving Projections (LPP) in retaining local manifold structure. To demonstrate the accuracy of the LPP-KCVA approach, Tennessee Eastman process (TEP) is applied, and KCVA, KPCA methods serve as the reference. The outcome illustrates its power in fault detection.
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