A Data-driven Fault Prediction Integrated Design Scheme Based on Ensemble Learning for Thermal Boiler Process

Abstract

As modern industrial systems are becoming more and more sophisticated, the reliability and safety issues of these complex industrial systems have become the most critical parts in system design. Data-driven fault diagnosis and fault prediction technology play important roles in the field of fault prediction and health management of complex industrial systems. This paper studies the machine learning aided data-driven fault prediction techniques. Three kinds of machine learning algorithms, i.e. random forest (RF), lasso regression (Lasso) and support vector machine regression (SVR), are employed to predict the fault related key performance indicator (KPI) of the thermal boiler system. The boiler's monitoring data is preprocessed, after which the characteristic variables are selected with the algorithm of RF and support vector machine-recursive feature elimination (SVM-RFE). The stacking algorithm is finally used to combine the three basic models. The proposed prediction model performs much better in fault prognosis in comparison with the original prediction model.