基于扩展窗口的时序不完备诊断方法研究

Extended Observation Window for Diagnosing Discrete-event Systems with Incomplete Event Sequence Model

离散事件系统诊断中,由于系统复杂度较高,对系统建模时要获得系统的完备行为非常困难。传统的诊断方法往往基于模型完备的假设,在模型不完备时会出现得不到诊断解释的问题。针对模型定义不完备中的一种情况——事件顺序定义不完备,提出了一种基于扩展窗口的时序不完备诊断方法,该方法利用相关事件无序信息,在增量诊断时通过动态改变观测窗口大小,结合两个观测窗口的观测序列,在一定程度上解决了不完备的诊断问题。该方法不仅扩展了模型完备条件的约束,得到了合理的诊断结果,而且改进了观测延迟导致的观测乱序情况,扩大了模型诊断的适用范围。最后,通过算法分析和实验结果证明该诊断方法在复杂度较低的情况下能够得到合理的诊断结果。

Most of traditonal approaches to fault diagnosis of discrete-event systems require a complete and accurate model of the system to be diagnosed. Aiming at this situation, we presented an approach to diagnose the incomplete event sequence model. Three aspects of the approach are adding information to the system model, dynamically extending observation window and merging the observations of the two windows. This diagnosis approach not only processes the event sequence model to expand the applicative scope, but also sloves the observation delay in a certain extent. It was tested. The result shows the approach in case of low complexity brings out expected results according to certain incom- plete models.