Simulation-Based Two-Stage Machine Learning Approach for Fault Detection and Severity Estimation in Industrial Gas Turbines

Heavy-duty industrial gas turbines accumulate performance degradation in the compressor and turbine sections during continuous operation, making reliable fault diagnosis essential for safe and efficient operation. This study presents a two-stage machine learning framework for fault detection and severity estimation, developed and evaluated on a single-shaft gas turbine model inspired by the GE 7HA.03. A damage propagation model, adapted from an established NASA methodology, simulated compressor fouling, turbine erosion, and their combined occurrence across three load conditions, generating 34,560 training and 2880 testing samples via Monte Carlo sampling of ambient conditions and multiplicative Gaussian noise injection. The first stage employs a calibrated, selective XGBoost classifier that withholds low-confidence predictions for manual review, achieving 98.6% classification accuracy at 94.7% coverage. The second stage uses a Multi-Task Learning multilayer perceptron with dedicated output heads for compressor and turbine degradation, achieving an aggregate R2 above 0.985 and a mean absolute error below 0.156 percentage points. Explainability analysis via SHAP and permutation importance confirmed that both stages rely on physically interpretable sensor signals. Finally, robustness testing across interpolated severity levels and elevated sensor noise defined the system’s operational boundaries, supporting its applicability to industrial condition monitoring.