Anomaly detection of steam turbine with hierarchical pre-warning strategy

Anomaly detection of steam turbines is to recognize infrequent instances within sensor data that plays a vital role in stable power supply. Machine learning models have been applied to diagnose the faults of turbine and verified useful for identifying engine problem. To detect anomalies of steam turbines with machine learning methods, here, an approach called hierarchical pre-warning strategy is proposed that combines clustering methods with classification methods. Three different clustering methods, K-means, Isolation Forest and Local Outlier Factor, are chosen to separate anomalies from normal data. Since clustering results cannot give unanimous decision, the clustering instances are labelled with three classes, real anomalies, suspected anomalies and normal data, according to their overlapping recognition. Subsequently, five classification algorithms, k-nearest neighbour, support vector machine, decision tree, random forest and gradient boosting decision tree, have been examined to train the labelled data set. The classification results illustrate that gradient boosting decision tree and random forest are much more precise to detect real anomalies of steam turbines. The real anomalies identified by clustering methods have been classified into suspected anomalies by this approach that is more practicable and consistent with ground truth.

Automated summary

Anomaly detection of steam turbines is crucial for stable power supply by recognizing infrequent instances within sensor data. This study proposes a hierarchical pre-warning strategy that combines clustering and classification methods for anomaly detection. Experimental results suggest that gradient boosting decision tree and random forest are more precise in detecting real anomalies of steam turbines.