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Model-based diagnosis of incomplete discrete-event system with rough set theory


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Fault diagnosis of discrete-event system (DES) is important in the preventing of harmful events in the system. In an ideal situation, the system to be diagnosed is assumed to be complete; however, this assumption is rather restrictive. In this paper, a novel approach, which uses rough set theory as a knowledge extraction tool to deal with diagnosis problems of an incomplete model, is investigated. DESs are presented as information tables and decision tables. Based on the incomplete model and observations, an algorithm called Optimizing Incomplete Model is proposed in this paper in order to obtain the repaired model. Furthermore, a necessary and sufficient condition for a system to be diagnosable is given. In ensuring the diagnosability of a system, we also propose an algorithm to minimize the observable events and reduce the cost of sensor selection.


  1. 1.

    将粗糙集理论与基于模型诊断相结合, 对不完备的离散事件系统进行修复;

  2. 2.

    在粗糙集理论下, 提出了系统可诊断的充分必要条件;

  3. 3.

    基于粗糙集理论, 在不改变系统可诊断性的前提下, 将离散事件系统的可观测事件极小化。

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Correspondence to Yonggang Zhang.

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Geng, X., Ouyang, D. & Zhang, Y. Model-based diagnosis of incomplete discrete-event system with rough set theory. Sci. China Inf. Sci. 60, 012205 (2017). https://doi.org/10.1007/s11432-015-0897-0

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  • model-based diagnosis
  • diagnosability
  • discrete-event system
  • finite state machine
  • rough set theory


  • 基于模型诊断
  • 可诊断性
  • 离散事件系统
  • 有限状态自动机
  • 粗糙集理论