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

Abstract

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.

Online Anomaly Detection of Distillation Tower System Using Adaptive Resonance Theory

“…Yamashita (2004) also proposed an algorithm for fault detection that is based on clustered classes from ART2 models and detected anomalies by the difference between recent class distribution and historical class distribution. Sekiai et al (2011) proposed an auto tuning algorithm for the vigilance parameter of ART2 models without using trial and error and empirical knowledge, and evaluated the performance of the proposed algorithm through several case studies with gas turbine plant data. As they described in their papers, an ART2 model creates new categories when status changes or anomalies occur.…”