Fault diagnosis for large-scale equipments in thermal power plant by data mining
火电厂大型设备故障诊断的数据挖掘方法(英文)

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.