摘要
针对实现主动维修的油液监测故障诊断技术中存在的不确定性问题以及知识获取和故障源挖掘的问题,将知识发现方法引入油液监测和故障诊断技术,采用粗糙集和决策树、贝叶斯网络相结合的算法,与联机分析有机结合,构建了基于油液监测的智能维护系统。通过ADO使用Visual C++操作SQL Server数据库构建知识发现与维修决策算法应用于系统的知识发现模型,列举了模型的应用实例并作了相应的分析。
To acquire diagnosis knowledge and discover the root cause of failure,this paper introduces knowledge discovery into oil monitoring diagnosis to solve the vast uncertain information in oil monitoring technology.Based on rough set,decision tree and Bayesian network are constructed for an intelligent maintenance system.Then,Visual C＋＋ is used to operate SQL Server through ADO to build a knowledge discovery and maintenance decision-making algorithm for the maintenance system and the results show that the diagnosis performance is improved by our system.An application example is given,and analysis.
出处
《机械科学与技术》
CSCD
北大核心
2010年第4期524-527,531,共5页
Mechanical Science and Technology for Aerospace Engineering
关键词
粗糙集
决策树
贝叶斯网络
油液监测
知识发现
rough set
decision tree
Bayesian networks
oil monitoring
knowledge discovery
