Diagnosis of feedwater heater performance degradation using fuzzy inference system

•We show how fuzzy inference systems can be used in plant diagnosis when it is necessary.•FIS are able to integrate expertise and rule learning from data into a single framework.•FIS has expandability to diagnosis widely. This means that Fuzzy inference system can be applied to all power plant models in the same way.•Consequently, more measurement inputs, accurate knowledge and elaborate composition of Fuzzy sets can allow for more accurate diagnosis of performance degradation. Power generation facilities cannot avoid performance degradation caused by severe operating conditions such as high temperature and high pressure, as well as the aging of facilities. Since the performance degradation of facilities can inflict economic on power generation plants, a systematic method is required to accurately diagnose the conditions of the facilities. This paper introduces the fuzzy inference system, which applies fuzzy theory in order to diagnose performance degradation in feedwater heaters among power generation facilities. The reason for selecting only feedwater heaters as the object of analysis is that it plays an important role in the performance degradation of power generation plants, which have recently been reported with failures. In addition, feedwater heaters have the advantage of using many data types that can be used in fuzzy inference because of low measurement limits compared to other facilities. Fuzzy inference systems consists of fuzzy sets and rules with linguistic variables based on expert knowledge, experience and simulation results to efficiently handle various uncertainties of the target facility. We proposed a method for establishing a more elaborate system. According to the experimental results, inference can be made with consideration on uncertainties by quantifying the target based on fuzzy theory. Based on this study, implementation of a fuzzy inference system for diagnosis of feedwater heater performance degradation is expected to contribute to the efficient management of power generation plants.