Journal
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
Volume 7, Issue 5, Pages 1279-1288Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2020.1003333
Keywords
Cyber-physical systems; discrete event systems; fault diagnosis; imperfect communication; imperfect observation; Industry 4.0; resilience
Categories
Funding
- National Science Foundation (NSF) [1832110, 2000320]
- Air Force Research Laboratory (AFRL) [FA8750-15-2-0116]
- Office of the Secretary of Defense (OSD) [FA8750-15-2-0116]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [2000320] Funding Source: National Science Foundation
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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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