火电厂大型设备故障诊断的数据挖掘方法(英文)

Fault diagnosis for large-scale equipments in thermal power plant by data mining

中文关键词  故障诊断  数据挖掘  粗糙集  属性约简  决策树

英文关键词  fault diagnosis  data mining  rough set  attribute reduction  decision tree

中文摘要

      针对火电厂大型设备的常见故障 ,提出一种新的诊断方法———数据挖掘方法 .该方法通过建立一个智能化的数据挖掘工具 ,直接从火电厂SCADA系统历史数据库的大量实时数据中获取故障诊断知识进行故障诊断 .数据挖掘工具的核心是 ,采用粗糙集的约简方式 ,将数据库中抽取的故障诊断规则简化为基于最小变量集的决策表 .该方法避免了为诊断故障而附加的专门测试或试验 ,降低了费用 ,同时减少了试验对设备造成的潜在危险 .将这一方法应用于火电厂锅炉的一个复杂故障事例 ,结果表明其诊断的精度在 92 %以上 ,可以满足现场应

英文摘要

      This paper proposes a new approach to diagnose frequent faults for large-scale equipments in thermal power plants.Based on the acquired data in SCADA (Supervisory control and data acquisition) systems,a hybrid-intelligence data-mining framework is developed to extract hidden diagnosis information.The hard core of the hybrid-intelligence data-mining framework is an algorithm in finding minimum size reduction which is based on rough set approach,which makes it possible to eliminate additional test or experiments for fault diagnosis which are usually expensive and involve some risks to the equipment.This approach is also tested by all the data in a SCADA system's database of a thermal power plant for boilers fault diagnosis.The decision rules'accuracy varied from 92 percent to 95 percent in different months.