A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Li, Boyuan; Diao, Xiaoxu; Vaddi, Pavan Kumar; Gao, Wei; Smidts, Carol

doi:10.1016/j.anucene.2021.108746

Title: A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Journal Article · Fri Oct 08 00:00:00 EDT 2021 · Annals of Nuclear Energy

DOI:https://doi.org/10.1016/j.anucene.2021.108746· OSTI ID:1976813

Li, Boyuan ^[1]; Diao, Xiaoxu ^[1]; Vaddi, Pavan Kumar ^[1]; Gao, Wei ^[1]; Smidts, Carol ^[1]

The Ohio State University, Columbus, OH (United States)

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. Here in this paper, a propagation-based fault detection and discrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the Integrated System Fault Analysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

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Research Organization:: North Carolina State University, Raleigh, NC (United States); The Ohio State University, Columbus, OH (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0000976

OSTI ID:: 1976813

Journal Information:: Annals of Nuclear Energy, Vol. 166, Issue C; ISSN 0306-4549

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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