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

This is the authors' abstract. We don't add key points for this paper.

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.