动态观测下离散事件系统的双模式可诊断性

Dual-Pattern Diagnosability of Discrete Event Systems under Dynamic Observation

摘要

双模式诊断要求系统在故障模式发生后，能够在关键模式发生之前做出正确决策从而避免重大损失，相比传统的故障诊断更具有实际应用价值。针对静态观测中传感器设置的高成本低效率问题，提出一种动态观测策略下离散事件系统双模式诊断的多项式算法。对离散事件系统在动态观测下的双模式诊断给出形式化定义，构造模式识别器，分别对故障模式和关键模式进行标记，并通过自动机复合构造验证器验证系统的双模式可诊断性，进而基于该验证器推导出离散事件系统在动态观测下实现双模式可诊断的充分必要条件。结论和实例表明，所提出的多项式算法不仅可以有效验证离散事件系统的双模式可诊断性，还能够通过动态调整传感器的触发设置，优化资源利用效率的同时增强系统的可诊断性能，为复杂系统双模式诊断提供一种高效经济的解决方案。

Abstract

Dual-pattern diagnosis compels the system to act correctly between a faulty pattern and a subsequent critical pattern, thereby avoiding major losses. This capability makes it more practically relevant than traditional fault diagnosis. A polynomial algorithm for verifying the dual-pattern diagnosis of discrete event systems under dynamic observation is proposed to address the high cost and low efficiency of sensor deployment under static observation. Firstly, a formal definition of dual-pattern diagnosis under dynamic observation is presented. Pattern recognizers are then constructed to label faulty and critical patterns, respectively. Subsequently, a verifier automaton is built through automaton composition to verify dual-pattern diagnosability, from which a sufficient and necessary condition is derived. Theoretical analysis of the case study show that the proposed polynomial algorithm not only effectively verifies the dual-pattern diagnosability of discrete event systems, but also optimizes resource utilization efficiency while strengthening system diagnosability by dynamically adjusting sensor trigger settings. This approach provides an efficient and economical solution for dual-pattern diagnosis of complex systems.

关键词

双模式诊断 / 离散事件系统 / 动态观测 / 多项式算法 / 验证器

Key words

dual-pattern diagnosis / discrete event system / dynamic observation / polynomial algorithm / verifier

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基金资助

国家自然科学基金项目(61673122)

广东省自然科学基金项目(2023A1515012783)