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

作　　者：Xuena GENG Dantong OUYANG Yonggang ZHANG 

机构地区：[1]Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun 130012, China [2]College of Computer Science and Technology, Jilin University, Changchun 130012, China

出　　处：《Science China(Information Sciences)》2017年第1期186-196,共11页中国科学（信息科学）（英文版）

基　　金：supported in part by National Natural Science Foundation of China (Grant Nos. 61272208, 61133011, 41172294, 61170092);Jilin Province Science and Technology Development Plan (Grant No. 201201011)

摘　　要：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.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.

关 键 词：model-based diagnosis DIAGNOSABILITY discrete-event system finite state machine rough set theory 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]