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In this paper, a novel kernel independent component analysis method which is named improved DKICA is proposed for dynamic industry processes' fault detection and fault diagnosis. The primary idea of this method is how to obtain an augmented measurement matrix in the data kernel space, the independent component analysis is used, so the dynamic and nonlinear features can be extracted in non-linear non-Gaussian...
Pneumatic control valves are the most frequently used actuators in industrial processes. Its property definitely affects the performance of processes and therefore process monitoring of the pneumatic control valve is of great importance. Canonical Variate Analysis (CVA) is a multivariate data-driven method which considers time correlations and has been demonstrated to be superior to some methods in...
In process monitoring of batch process, Fisher discriminant analysis is a very popular method and has be widely applied. In this paper, a new kernel local Fisher discriminant analysis (KLFDA) algorithm is proposed for fault diagnosis. The main contributions of the presented approach are as follows: 1) the proposed algorithm can simultaneously extract the global European distribution of data and local...
Monitoring of dynamic industrial process has been increasingly important due to more and more strict safety and reliability requirements. Popular methods like time lagged arrangement-based and subspace-based approaches exhibit good performance in fault detection, however, they suffer from difficulty in accurately isolating faulty variables and diagnosing fault types. To alleviate this difficulty,...
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