Anomaly Detection in Gas Turbines Based on Semisupervised Learning Framework

Currently, gas turbines are finding increasingly widespread applications. To ensure their safe and stable operation, condition monitoring and anomaly detection are crucial. However, traditional anomaly detection methods for gas turbines often rely solely on a single learning approach. To address these issues, this paper proposes a novel semisupervised learning framework that synergistically combines the large-scale automatic labeling capability of unsupervised learning with the precise classification power of supervised learning. First, an unsupervised learning algorithm is employed to hierarchically label anomalies in real operational data as suspicious anomalies, high-probability anomalies, and actual anomalies. Next, oversampling techniques are applied to address class imbalance issues by augmenting underrepresented classes in the dataset. Finally, supervised learning methods are utilized to train models on the labeled samples, with their performance compared against other machine learning (ML) approaches. Through comparative analysis of multiclass classification evaluation metrics, the feasibility of the proposed semisupervised learning framework is demonstrated, and the optimal monitoring model is identified. The core contribution is providing a semi-supervised learning framework that categorizes operational data into a multitier hierarchy to enable a nuanced early warning mechanism.