Article ID Journal Published Year Pages File Type
6858487 Information Sciences 2014 11 Pages PDF
Abstract
This paper proposes a novel approach for dealing with fault detection of multivariate processes, which will be referred to as kernel dynamic independent component analysis (KDICA). The main idea of KDICA is to carry out an independent component analysis in the kernel space of an augmented measurement matrix to extract the dynamic and non-linear characteristics of a non-linear non-Gaussian dynamic process. Furthermore, as a new method of fault diagnosis, a non-linear contribution plot is developed for KDICA. A comparative study on the Tennessee Eastman process is carried out to illustrate the effectiveness of the proposed method. The experimental results show that the proposed method compares favorably with existing methods.
Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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