An efficient ICA-DW-SVDD fault detection and diagnosis method for non-Gaussian processes

Mu-Chen Chen*, Chun Chin Hsu, Bharat Malhotra, Manoj Kumar Tiwari

*此作品的通信作者

研究成果: Article同行評審

18 引文 斯高帕斯(Scopus)

摘要

Independent Component Analysis (ICA) has been extensively used for detecting faults in industrial processes. While applying ICA to process monitoring, the inability of identifying the important components affect the fault diagnosis ability. For further improving the competence of ICA, this paper proposes an approach integrating ICA, Durbin Watson (DW) criterion and Support Vector Data Description (SVDD) to monitor non-Gaussian process for detecting faults. In the proposed approach, namely ICA–DW–SVDD, ICA is a non-Gaussian information extractor from original variables, DW identifies dominating ICs, and SVDD plays the role of fault detector. This paper also discusses the retracing method to detect original variables causing disturbance in the process. One simulation case and the Tennessee Eastman Process are used to demonstrate the effectiveness of our proposed approach.

原文English
頁(從 - 到)5208-5218
頁數11
期刊International Journal of Production Research
54
發行號17
DOIs
出版狀態Published - 1 九月 2016

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