摘要
在提升机故障诊断中,从包含冗余和不一致信息的数据中获取简单有效的诊断决策规则是一个难题。本文中提出了一种基于粗糙集理论的提升机故障诊断规则获取模型。该模型使用基于粗糙集理论的启发式算法对诊断规则进行简约,从而生成诊断规则集,建立了用于故障诊断的规则库。通过对提升机故障诊断的仿真实例表明,该方法有效地简化了特征参数和诊断规则,减低了诊断成本,提高了故障诊断的准确率。
Extraction of simple and effective decision rules for fault diagnosis is one of the most important issues needed to be addressed in mine hoist, because available information is often inconsistent and redundant. This paper presents a fault diagnosis model based on rough set theory. From original fault data containing inconsistent and redundant information, a set of maximal generalized decision rules with certainty factor and coverage factor are generated by using a proposed value reduction algorithm, and therefore a decision rules base for fault diagnosis is established. Simulation results for mine hoist show that the method improves the rate of fault diagnosis, decreases the number of feature parameters and diagnostic rules, and reduces the cost of diagnosis.
出处
《微计算机信息》
北大核心
2008年第34期147-149,共3页
Control & Automation
基金
煤矿安全监测数据解析整合模型研究及应用
国家基金委颁发
国家自然科学基金项目(50674086)
全矿井安全监测数据解析整合模型研究与应用
教育部颁发
高等学校博士学科点专项科研基金项目(20060290508)
江苏省社会发展科技计划项目
江苏省教委颁发
江苏省科技计划基金(BS2006002)
关键词
故障诊断
粗糙集
知识约简
规则提取
fault diagnosis
rough sets
knowledge reduction
rules extraction
