Comparison of structural analysis and principle component analysis for leakage prediction on superheater in boiler

Abstract

Leakage is one of the failures which commonly happens in boiler operation. Moreover, a continuous unsettled anomaly in a boiler could lead to leakage failure. An algorithm has been developed to predict the failure, consisting of three general procedures: feature selection, followed by hierarchical clustering, and naïve Bayes classification. The hierarchical clustering changes unlabeled data into labeled data, and naïve Bayes classification calculates the probability to justify anomaly occurrence. Meanwhile, this research focused on the effect of the feature selection method on the result of leakage prediction. Two different feature selection methods, namely the structural analysis and the principal component analysis (PCA), were deployed separately and then compared. The result showed that leakage prediction using the structural analysis method gave 13 hours 40 minutes of prediction time, and the PCA method gave 25 hours of prediction time. However, the PCA feature selection method caused more false alarms than feature selection with structural analysis, which only triggered five false alarms a week before leakage. Moreover, the structural analysis offered better traceability than PCA to understand the leakage occurrence.