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Volume 7 Issue 5
Sep.  2020

IEEE/CAA Journal of Automatica Sinica

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Alejandro White, Ali Karimoddini and Mohammad Karimadini, "Resilient Fault Diagnosis Under Imperfect Observations–A Need for Industry 4.0 Era," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1279-1288, Sept. 2020. doi: 10.1109/JAS.2020.1003333
Citation: Alejandro White, Ali Karimoddini and Mohammad Karimadini, "Resilient Fault Diagnosis Under Imperfect Observations–A Need for Industry 4.0 Era," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1279-1288, Sept. 2020. doi: 10.1109/JAS.2020.1003333

Resilient Fault Diagnosis Under Imperfect Observations–A Need for Industry 4.0 Era

doi: 10.1109/JAS.2020.1003333
Funds:  This work was supported by the National Science Foundation (NSF) (1832110 and 2000320) and Air Force Research Laboratory (AFRL) and Office of the Secretary of Defense (OSD) (FA8750-15-2-0116)
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  • In smart industrial systems, in many cases, a fault can be captured as an event to represent the distinct nature of subsequent changes. Event-based fault diagnosis techniques are capable model-based methods for diagnosing faults from a sequence of observable events executed by the system under diagnosis. Most event-based diagnosis techniques rely on perfect observations of observable events. However, in practice, it is common to miss an observable event due to a problem in sensor-readings or communication/transmission channels. This paper develops a fault diagnosis tool, referred to as diagnoser, which can robustly detect, locate, and isolate occurred faults. The developed diagnoser is resilient against missed observations. A missed observation is detected from its successive sequence of events. Upon detecting a missed observation, the developed diagnoser automatically resets and then, asynchronously resumes the diagnosis process. This is achieved solely based on post-reset/activation observations and without interrupting the performance of the system under diagnosis. New concepts of asynchronous detectability and asynchronous diagnosability are introduced. It is shown that if asynchronous detectability and asynchronous diagnosability hold, the proposed diagnoser is capable of diagnosing occurred faults under imperfect observations. The proposed technique is applied to diagnose faults in a manufacturing process. Illustrative examples are provided to explain the details of the proposed algorithm. The result paves the way towards fostering resilient cyber-physical systems in Industry 4.0 context.

     

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    Highlights

    • The paper develops a diagnosis tool which can robustly detect, locate, and isolate faults.
    • The developed diagnoser is resilient against missed observations.
    • In case of missed observation, the diagnoser automatically resets and asynchronously resumes the diagnosis process.
    • New concepts of asynchronous detectability and asynchronous diagnosability are introduced.
    • It is formally proved that the proposed diagnoser is capable of diagnosing faults under imperfect observations.

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