论文标题

分散状态推断和故障诊断/预测离散事件系统的统一方法

A unified method to decentralized state inference and fault diagnosis/prediction of discrete-event systems

论文作者

Zhang, Kuize

论文摘要

国家推论问题和故障诊断/预测问题是许多领域的基本主题。在本文中,我们考虑由有限状态自动机(FSA)建模的离散事件系统(DESS)。分散版本的后一个问题存在结果,但是对于以前的问题的分散版本几乎没有结果。我们提出了一个称为共检测性的强可检测性的分散版本,这意味着一旦系统满足该属性,对于每个生成的无限长度事件序列,至少一个局部观察者可以在常见观察时间延迟后确定当前和后续状态。我们证明,验证FSA的共探可发现性的问题是困难的。此外,我们使用一种统一的并发结构方法来提供PSPACE验证算法,以提供FSA的共同检测性,可诊断性和共同预测性,而没有任何假设或修改所考虑的FSA,在[Debouk&Lafortune&Lafortune&Laefter&Tenekets 2000时首次研究了共同诊断性,同时[debouk&laforne&teneket&teneket&teneket&teneket&teneket&teneket&teneket&teneket&tenekits 2000] Takai 2010]。通过我们提出的统一方法,可以看到,为了验证共探可检测性,与验证其他两个属性相比,将遇到更多的技术困难,因为在共同检测性中,计算了生成的输出,但在后两个属性中,仅计算事件的出现。例如,当生成一个输出时,可能会发生任何数量的不可观察的事件。在文献中已经知道了验证共诊断性的pspace固定性。在本文中,我们证明了验证共检测性的pspace坚持性。

The state inference problem and fault diagnosis/prediction problem are fundamental topics in many areas. In this paper, we consider discrete-event systems (DESs) modeled by finite-state automata (FSAs). There exist results for decentralized versions of the latter problem but there is almost no result for a decentralized version of the former problem. We propose a decentralized version of strong detectability called co-detectability which implies that once a system satisfies this property, for each generated infinite-length event sequence, at least one local observer can determine the current and subsequent states after a common observation time delay. We prove that the problem of verifying co-detectability of FSAs is coNP-hard. Moreover, we use a unified concurrent-composition method to give PSPACE verification algorithms for co-detectability, co-diagnosability, and co-predictability of FSAs, without any assumption or modifying the FSAs under consideration, where co-diagnosability is firstly studied by [Debouk & Lafortune & Teneketzis 2000], while co-predictability is firstly studied by [Kumar \& Takai 2010]. By our proposed unified method, one can see that in order to verify co-detectability, more technical difficulties will be met compared to verifying the other two properties, because in co-detectability, generated outputs are counted, but in the latter two properties, only occurrences of events are counted. For example, when one output was generated, any number of unobservable events could have occurred. The PSPACE-hardness of verifying co-diagnosability is already known in the literature. In this paper, we prove the PSPACE-hardness of verifying co-predictability.

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