Data Mining And Its Application In Fault Diagnosis Of Power Plant Condenser

Knowledge acquisition for power plant intelligent fault diagnosis is the bottleneck constructing expert systems. Power plant have advanced DCS system, they collected a large number of data. But the valuable information hidden behind of the data is not mind and used effectively. Data mining techniques can intelligently and automatically discover knowledge from database. In this paper, data mining is applied to acquire knowledge to solve the problem of the Knowledge acquisition for expert systems.Rough Set theory is a powerful tool in deal with vagueness and irrelevant information. It can be used to reduce features and extract rules. In this paper, rough set theory is firstly applied to extract features of the power plant condenser. The test verifies that it is drastically effective.In the paper, Identification Data method is firstly applied to realized the condenser fault diagnosis. Samples show that this method is effective for the condenser fault diagnosis.The condenser fault diagnosis is a complicated non-linear mapping. Neural networks are widely applied to pattern recognition. They can map complicated non-linear functions at infinite precision. In the paper, BP networks are trained as a classifier of the condenser fault. The test shows validity of the classifier is high.Association rules is to discovery association between sets of items in large database. In the paper, association rules is applied to discover association between the equipment alarms. It is very significance.