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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...
Fault detection and isolation (FDI), which is a critical part of modern industrial systems, plays a key role in the maintainability, safety, and reliability of processes. Existing FDI approaches are dependent on varying degrees of knowledge of the process, limiting their implementation in practical industrial processes. Based on the least absolute shrinkage and selection operator (lasso), this paper...
Available sensing measurements in modern industrial process include two significant characteristics: distribution and autocorrelation. Different types of sensing measurements exhibit different characteristics. Moreover, different feature extraction methods are suitable for data with corresponding characteristics. This paper proposes a novel dual-step subspace partition method in order to establish...
Principal component analysis (PCA) is widely used for fault detection for chemical processes; however, the efficient principal component (PC) selection remains an challenge. The effect of PC selection on PCA-based fault detection performance is analyzed within the statistical framework of hypothesis testing. A performance-driven fault-relevant PC (FRPC) subspace construction integrated with Bayesian...
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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