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不确定观测下离散事件系统的可诊断性 被引量:4

Diagnosability of Discrete-Event Systems with Uncertain Observations
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摘要 从系统诊断的角度来看,可诊断性是离散事件系统的一个重要性质.其要求系统发生故障后经过有限步的观测可以检测并隔离故障.为简单起见,对离散事件系统可诊断性的研究大都假定观测是确定的,即观测到的事件序列与系统实际发生的可观测事件序列一致.而在实际应用中,由于感知器的精度、信息传输通道的噪声等原因,所获取的观测往往是不确定的.重点研究观测不确定条件下离散事件系统的可诊断性问题.首先扩展了传统可诊断性的定义,定义了观测不确定条件下的可诊断性;然后,分别给出各类观测不确定条件下的可诊断性判定方法;在更一般的情况下,各类观测不确定可能共同存在,因此,最后给出一般情况下的可诊断性判定方法. Diagnosability is an important property of discrete-event system (DES) from the perspective of diagnosis. It requires that every fault can be detected and isolated within a finite number of observations after its occurrence. In numerous literatures, diagnosability is studied under the assumption that an observation is certain, i.e., the observation corresponds to the sequence of observable events exactly taking place in the DES. But in practical applications, the assumption may become inappropriate. Due to various reasons such as the precision of sensors and noises in transmission channels, the available observation may be uncertain. This paper focuses on the diagnosability of DESs with uncertain observations. It extends the definition of diagnosability to cope with uncertain observations. Methods are given to check the diagnosability with three types of uncertain observations accordingly. In a more general scenario where multiple uncertainties exist in the observation, a method is also provided to check the diagnosability with all the uncertainties of the observation together.
出处 《软件学报》 EI CSCD 北大核心 2017年第5期1091-1106,共16页 Journal of Software
基金 国家自然科学基金(61603152 61463044 61363030) 广西可信软件重点实验室研究课题(KX201604 KX201606 KX201419 KX201330) 贵州省科技厅项目(LH[2014]7421) 广西自然科学基金(2015GXNSFAA139285)~~
关键词 不确定观测 离散事件系统 可诊断性 uncertain observation discrete-event system diagnosability
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  • 1栾尚敏,戴国忠.利用结构信息的故障诊断方法[J].计算机学报,2005,28(5):801-808. 被引量:24
  • 2陈琳,黄杰,龚正虎.一种求解最小诊断代价的小生境遗传算法[J].计算机学报,2005,28(12):2019-2026. 被引量:6
  • 3Hamscher W, Console L, de Kleer J. Readings in Modelbased Diagnosis. San Mateo, CA: Morgan Kaufmann, 1992.
  • 4Console L, Dressier O. Model-based diagnosis in the real world: lessons learned and challenges remaining. In: Proceodings of the 16th International Joint Conference on Artificial Intelligence (IJCAI-99). Sweden: Morgan Kaufmann, 1999. 1393-1400.
  • 5del Val A. On some tractable classes in deduction and abduction. Artfticial Intelligence, 2000, 116(1-2) : 297-313.
  • 6del Val A. The complexity of restricted consequence finding and abduction. In: Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-00). USA: AAAI Press/The MIT Press, 2000. 337-342.
  • 7Nordh G, Zanuttini B. Propositional abduction is almost always hard. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (UCAI-05). UK: Professional Book Center, 2005. 534-539.
  • 8Eiter T, Makino K. On computing all abductive explanations from a propositional Horn theory. Journal of the ACM, 2007, 54(5) : article 24, 1-54.
  • 9Stress P. Model-based and qualitative reasoning: an introduction. Annals of Mathematics and Artificial Intelligence, 1997, 19(3-4) : 355-381.
  • 10Console L, Picardi C, Theseider Dupre D. A framework for decentralized qualitative model-based diagnosis. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence ( IJCAI-07 ). India: AAAI Press, 2007. 286-291.

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