This article addresses the combinatorial explosion problem for diagnosability analysis of discrete event systems (DESs) using bounded labeled Petri nets (LPNs). Some reduction rules are given to simplify a priori the LPN model before analyzing the diagnosability. When the conditions of these reduction rules are fulfilled, some regular unobservable transitions and some specific observable transitions are suppressed. It is proven that these rules preserve the diagnosability property of the LPN system. By using reduction rules, the memory cost for diagnosability analysis is reduced. Note to Practitioners-Fault diagnosis based on discrete-event systems has been successfully used in several fields of applications, such as transportation, telecommunication, manufacturing, and so on. At the design stage of a system, the diagnosability needs to be held, which refers to the ability to determine if the system can detect the fault after its occurrence. Therefore, the diagnosability is a critical property due to its importance in terms of safety of an industrial system. In order to allow an industrial exploitation of diagnosability analysis, this article proposes reduction rules, which make it possible to reduce a priori the size of the LPN model of an industrial system.

中文翻译：

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标记Petri网建模的复杂系统可诊断性分析的约简规则


本文解决了使用有界标记 Petri 网 (LPN) 进行离散事件系统 (DES) 诊断性分析的组合爆炸问题。在分析可诊断性之前，给出了一些简化规则来先验地简化LPN模型。当满足这些约简规则的条件时，一些常规的不可观察的转变和一些特定的可观察的转变将被抑制。事实证明，这些规则保留了 LPN 系统的可诊断性。通过使用缩减规则，可减少可诊断性分析的内存成本。从业者须知——基于离散事件系统的故障诊断已成功应用于交通、电信、制造等多个应用领域。在系统的设计阶段，需要保持可诊断性，即故障发生后判断系统是否能够检测到故障的能力。因此，可诊断性因其对于工业系统的安全性的重要性而成为关键属性。为了允许对可诊断性分析进行工业开发，本文提出了简化规则，从而可以先验地减小工业系统的 LPN 模型的大小。