Robust Fault Diagnosis of Stochastic Discrete Event Systems
Robust Fault Diagnosis of Stochastic Discrete Event Systems
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DOI：
10.1109/tac.2019.2893873
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发表时间：
2019-01
影响因子：
6.8
通讯作者：
Xiang Yin;Jun Chen;Zhaojian Li;Shaoyuan Li
中科院分区：
文献类型：
--
作者：
Xiang Yin;Jun Chen;Zhaojian Li;Shaoyuan Li
研究了随机离散事件系统对模型不确定性的鲁棒故障诊断问题。在这个问题中，我们假设系统的实际行为是先验未知的，并且系统的真实模型属于由概率自动机描述的一组可能的模型。这个问题的目标是几乎成功地检测到故障的发生，在这个意义上，第一，没有假警报可以作出，第二，误检率小于一个给定的阈值$\N $后，一些延迟$K$，即使不知道真正的模型先验。提出了一个鲁棒$（\k，K）$-可诊断性的条件来刻画满足上述要求的鲁棒诊断器的存在性。我们还提出了鲁棒$\N $-可诊断性和鲁棒A-可诊断性的概念，这要求给定的误检率$\N $可以实现一定的延迟和任何任意小的误检率可以实现，分别。对于每一种情况，也提出了一个有效的验证算法。我们的结果推广了以往的随机离散事件系统的故障诊断工作，考虑模型的不确定性和特定的误检率。
We investigate the problem of robust fault diagnosis of stochastic discrete-event systems against model uncertainty. In this problem, we assume that the actual behavior of the system is unknown a priori and the true model of the system belongs to a set of possible models described by probabilistic automata. The goal of this problem is to almost successfully detect the occurrence of fault in the sense that, first, no false alarm can be made, and second, the misdetection rate is smaller than a given threshold $\epsilon$ after some delay $K$ even without knowing the true model a priori. A condition termed as robust $(\epsilon,K)$-diagnosability is proposed to capture the existence of such a robust diagnoser that satisfies the above-mentioned requirements. We also propose the notions of robust $\epsilon$-diagnosability and robust A-diagnosability, which require that a given misdetection rate $\epsilon$ can be achieved with some delay and any arbitrarily small misdetection rate can be achieved, respectively. For each condition, an effective verification algorithm is also proposed. Our results generalize previous works on fault diagnosis of stochastic discrete-event systems by taking model uncertainty and specific misdetection rate into account.