Automatic determination of optimal fault detection filter. (October 2022)

• Record Type: Journal Article
• Authors: Zhou, Jinming; Zhu, Yucai
• Is Part Of: Journal of process control. Volume 118(2022)
• Page Start: 69
• Page End: 81
• Publication Date: 2022-10
• Subjects: Fault detection -- Filter design -- Kernel density estimation -- Tennessee Eastman process -- System identification; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.08.009
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Optimal detection filters can greatly enhance fault detection performance, but designing these filters requires fault data which is difficult to obtain in practice. This paper proposes a scheme that automatically determines the optimal detection filter from a filter bank online without using fault data. The method can improve fault detection rate and accelerate detection speed. In order to reduce the false alarm rate, a method of threshold setting is introduced based on kernel density estimation. Implementation issues concerning filter bank design and online decision rule are also discussed. The method is validated in a numerical example and Tennessee Eastman process, and its performance is compared to those of other state-of-the-art methods. Highlights: Enabling automatic determination of optimal fault detection filter. Using kernel density estimation to refine the thresholds. Enhancing fault detection performance without using fault data. Performance validated in Tennessee Eastman benchmark.