Auto Tuning Algorithm for Vigilance Parameter in the Adaptive Resonance Theory Model and its Application to Fault Diagnosis System of Thermal Power Plants

In order to operate thermal power plants safely, early detection of equipment failure signs is one of the most important issues. To detect the signs before an alarm is issued in the existing monitoring system, we developed a fault diagnosis system based on the Adaptive Resonance Theory (ART). The vigilance parameter, which is a design parameter in the ART model, was shown to influence the diagnosis accuracy. Fixing the value of the vigilance parameter also had problems: we needed to use time-consuming trial and error, and we needed to have empirical knowledge of the parameter tuning. In this paper, using simulations we demonstrated the relationship between the vigilance parameter and diagnosis accuracy. Furthermore, to overcome the problems of the vigilance parameter tuning, we have proposed an auto tuning algorithm to make the parameter the optimum value. The performance of the proposed algorithm was evaluated in several case studies using gas turbine plant data. The effectiveness of the proposed algorithm was confirmed by the obtained results.