Knowledge Discovery Method of Incomplete Fault Diagnosis Information via Valued Characteristic Relation
-
摘要: 为了从包含多种未知属性值的不完备故障诊断信息中获取决策规则,提出一种量化特征关系用于不完备故障诊断的知识获取方法。首先,结合不完备故障诊断信息产生的原因,确定未知属性值的类型;然后,利用量化特征关系对不完备故障诊断信息进行分析;最后,利用量化特征关系下属性约简算法获取故障诊断决策规则。结合故障齿轮箱的诊断实例验证了该方法的有效性,结果表明此方法可以从包含三种未知属性值的不完备故障诊断决策表中直接获取准确的故障诊断决策规则。Abstract: In order to discover decision rules from incomplete fault diagnosis information containing multiple unknown attribute values, a knowledge discovery method of incomplete fault diagnosis information by using valued characteristic relation is proposed. Firstly, the unknown attribute value types are determined according to the reasons for incomplete information. Secondly, the incomplete fault diagnosis information is analyzed by using the valued characteristic relation. Finally, the decision rules for fault diagnosis are discovered according to the attribute reduction algorithm based on the valued characteristic relation. The effectiveness of the method is demonstrated with the diagnosis case of faulty gearbox. The experimental results show that the present method can directly discover the accurate decision rules from incomplete fault diagnosis information with three categories of unknown attribute values.
-
[1] 邓林峰,赵荣珍.基于粒计算的知识获取方法研究及其应用[J].机械科学与技术,2011,30(7):1093-1097 Deng L F, Zhao R Z. A method for knowledge acquisition based on Granular computing and its application[J]. Mechanical Science and Technology for Aerospace Engineering, 2011,30(7):1093-1097 (in Chinese) [2] Pawlak Z. Rough sets[J]. International Journal of Computer & Information Sciences, 1982,11(5):341-356 [3] Kryszkiewicz M. Rough set approach to incomplete information systems[J]. Information Sciences, 1998,112(1-4):39-49 [4] Stefanowski J, Tsoukiàs A. Valued tolerance and decision rules[C]//Second International Conference on Rough Sets and Current Trends in Computing. Berlin Heidelberg: Springer, 2001:212-219 [5] Grzymala-Busse J W, Clark P G, Kuehnhausen M. Generalized probabilistic approximations of incomplete data[J]. International Journal of Approximate Reasoning, 2014,55(1):180-196 [6] Deng W, Yang X H, Zou L, et al. An efficient fusion approach to rule extraction based on rough set theory and particle swarm optimization and its application[J]. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2012,226(7):904-913 [7] Wang M, Hu N Q, Qin G J. A method for rule extraction based on granular computing: application in the fault diagnosis of a helicopter transmission system[J]. Journal of Intelligent & Robotic Systems, 2013,71(3-4):445-455 [8] Dong H L, Wang Z D, Chen X M, et al. A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information[J]. Mathematical Problems in Engineering, 2012:1-15 [9] 张尔卿,傅攀,李威霖.不完备先验知识下的刀具磨损状态评估方法研究[J].机械科学与技术,2015,34(3):418-422 Zhang E Q, Fu P, Li W L. Tool wear condition assessment based on incomplete priori knowledge[J]. Mechanical Science and Technology for Aerospace Engineering, 2015,34(3):418-422 (in Chinese) [10] Wang G Y, Guan L H. Data-driven valued tolerance relation[C]//7th International Conference on Rough Sets and Knowledge Technology. Berlin Heidelberg: Springer, 2012:11-19 [11] Li M, Deng S B, Feng S Z, et al. Fast assignment reduction in inconsistent incomplete decision systems[J]. Journal of Systems Engineering and Electronics, 2014,25(1):83-94
点击查看大图
计量
- 文章访问数: 135
- HTML全文浏览量: 24
- PDF下载量: 5
- 被引次数: 0