Automatic determination of optimal fault detection filter. (October 2022) - Zhou, Jinming; Zhu, Yucai
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.