Application of ensemble learning approach on anomaly detection of a dual induced draft fan system

Abstract

Intelligent monitoring of thermal power plants has been receiving growing interest. As one of the promising technologies in intelligent monitoring, machine learning-based fault detections for auxiliary equipment in the thermal power plant have been widely investigated. In this work, we constructed normal behavior models to predict the current of a dual-induced draft fan system by gradient-boosting tree regression. For anomaly detection, we evaluated the residual by calculating the mean value and meanwhile added a control boundary based on statistical analysis. As a result, we successfully identified the abnormal fan in the dual-induced draft fan system and assisted in the schedule of maintenance work.