Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 18, Issue 10, Pages 6828-6839Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2022.3147796
Keywords
Probability distribution; Probabilistic logic; Stochastic processes; Integrated design; Informatics; Generators; Uncertainty; Data-driven; distributionally robust optimization; fault detection (FD); integrated design; stable kernel representation (SKR)
Categories
Funding
- National Natural Science Foundation of China [62103247, 62033008, 61873149, 61733009]
- Chinese Postdoctoral Science Foundation [2021M702022]
- Research Fund for the Taishan Scholar Project of Shandong Province of China
- Postdoctoral Program for Innovative Talents of Shandong Province of China [SDBX2021010]
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In this article, an integrated design diagram for a stable kernel representation (SKR)-based data-driven fault detection (FD) system and performance criteria is proposed. The developed FD system is robust against the distributional uncertainties of noises and random faults. Experimental results demonstrate the applicability of the proposed method.
In this article, an integrated design diagram for a stable kernel representation (SKR)-based data-driven fault detection (FD) system and performance criteria is proposed for stochastic dynamic systems in the probabilistic sense. A new distributionally robust FD system is developed using input and output data in the absence of a system model and perfect probability distributions for noises and random faults. To be specific, an SKR-based data-driven primary residual generator is first constructed. By introducing the so-called mean-covariance based ambiguity sets, families of probability distributions of the primary residual in fault-free and the concerned multiple faulty cases are characterized. The FD system design is then formulated as a distributionally robust optimization problem in the sense of minimizing the missed detection rate (MDR) with a predefined upper bound of false alarm rate (FAR). With the aid of worst-case conditional value-at-risk, a matrix-valued distribution independent solution to the targeting FD problem is derived without posing specific distribution assumptions. The developed FD system is, thus, robust against the distributional uncertainties of noises and random faults. Simultaneously, a tighter upper bound of MDR for an identical FAR criterion is achieved in comparison with the vector-valued distributionally robust FD method. An experimental study on a laboratory setup of a three-tank system shows the applicability of the proposed method.
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