Robust Fault Prognosis of Discrete-Event Systems Against Loss of Observations

Xiao, Cuntao; Liu, Fuchun

doi:10.1109/tase.2021.3049400

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2022

2024

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Cited by 4 publications

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“…In [10], the authors showed how to use one prognoser to predict the occurrence of any failure for a set of models. Xiao and Liu [11] considered the problem of robust fault prognosis against loss of observations, where some observable events may become unobservable because of sensor failures. Finally, the problem of predictability is investigated in [12][13][14] in the framework of Petri nets.…”

Section: Introductionmentioning

confidence: 99%

Robust predictability in discrete event systems under sensor attacks

Zhang

2023

Front. Phys.

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The problem of robust predictability against sensor attacks is investigated. The objective of a diagnoser is to predict the occurrence of a critical event of a discrete event system (DES) under partial observation. An attacker may rewrite the diagnoser observation by inserting fake events or erasing real events. Two novel structures, namely, real diagnoser and the fake diagnoser, are constructed based on the diagnoser of the system. We compute the hybrid diagnoser as the parallel composition of the real diagnoser and the fake diagnoser. The hybrid diagnoser can be used to verify if a critical event of the system is robustly predictable when an attacker tampers with the diagnoser observation.

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Section: Introductionmentioning

confidence: 99%