Anomaly detection of steam turbine with hierarchical pre‐warning strategy

Abstract

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.

Machine learning for fault analysis in rotating machinery: A comprehensive review

“…Such test beds are also denoted as machine fault simulators. Some other built an experimental setup to represent the rotating machines or directly consider the real-world rotating machines, including wind turbines [ 13 , 99 , 125 , 130 , [158] , [159] , [160] ], rotor test rigs of aircraft [ 8 ], compressors [ [161] , [162] , [163] ], gas turbines [ 164 , 165 ], steam turbines [ 166 , 167 ], pumps [ [168] , [169] , [170] ], train bogie [ 171 ] and other rotating machine-included systems such as unmanned underwater or aerial vehicles [ 8 , 172 ] for machine learning-based fault analysis. Some recent works regarding the fault diagnosis of the rotating machines mentioned above are presented as follows.…”

Steam Turbine Anomaly Detection: An unsupervised learning using Enhanced LSTM Variational Autoencoder

“…However, the development of anomaly detection technology utilizing health data offers a promising solution. It circumvents the need for exhaustive historical labeled datasets across all fault categories, yet it holds profound implications for enhancing the reliability and stability of thermal power generation systems [3].…”

A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines

“…Zhou et al [25] created a BP neural network-based early warning algorithm for thermal fault diagnosis in electrical equipment, demonstrating better prediction accuracy. Yao et al [26] combined clustering and classification methods to develop a fault warning system for steam turbines. He et al [27] developed a multi-module emerging fault warning method for thermal power plants, suitable for scenarios with few operational samples.…”