Diagnostics and Prognostics of Boilers in Power Plant Based on Data-Driven and Machine Learning

Abstract

This paper reports diagnostics and prognostics study of boiler in power plant using actual boiler operating data. This study aims to early detect anomalies that occur in the boiler and to predict the remaining useful life (RUL) after anomalies are detected. The proposed method utilizes machine learning techniques through support vector machine (SVM) and random forest algorithm (RFA) for anomaly detection and similarity-based method of dynamic time warping (DTW) for RUL prediction. The developed method is validated by testing the prediction models using real operating data acquired from three boilers in power plant. The results show that some anomalies are successfully detected by prediction model even though there are anomalies that give low accuracies in predictions. RUL prediction also provides fair results given the limitations of the real data used in building prediction models. Overall, the results of this study have potential to be applied in real system as an auxiliary tool in the boiler condition monitoring to support boiler maintenance programs.