RESEARCH OF SELF-LEARNING PETRI NETS MODEL FOR FAULT DIAGNOSIS BASED ON RULE GENERATION

摘要：

Depending on the diagnostic rules derived from the default rule generation method of Skowron, a technique to establish Petri net model for fault diagnosis is researched in this paper.In order to simplify the Petri nets model, rule generation need the reduced sample set However, the reduction of the sample set may cause some errors because of the incompletion of the set.The method can resolve the problem and empower the model the ability of self-learning.The model can auto-update the structure and incidence matrix of the Petri net when diagnostic rules are changed.The method is proved to be available by an example about rotating machinery fault diagnosis in the paper.

关键词： Fault diagnosis Petri net Incidence matrix Diagnostic rule

作者: XI-LIN ZHAO JIAN-ZHONG ZHOU HUI LIU

作者单位: College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wu College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wu

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 1106-1110

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）