<![CDATA[This study systematically evaluates the effectiveness of classical machine learning models for gas turbine fault detection under class-imbalanced operating conditions. Using an industrial dataset of 1,386 observations with a binary target (30.7% Fault) and nine operational parameters, we first conduct exploratory analysis to characterize correlation structure and extreme operating states. The methodological pipeline comprises stratified train–test splitting, feature standardization, and training-set rebalancing using SMOTE, followed by estimation of four models: Logistic Regression, Random Forest, XGBoost, and Support Vector Machine. Model performance is assessed using common classification indicators, focusing on the trade-off between overall discrimination and the ability to correctly identify Fault conditions. The results show consistently weak discriminative power, with AUC values only slightly above random classification (0.48–0.55) and low effectiveness in detecting Fault cases, despite reasonable accuracy for No Fault conditions. These findings provide an empirical baseline showing that, for this dataset, classical models struggle to achieve clinically meaningful separation between normal and faulty turbine states. The study’s main contribution is to demonstrate, on real industrial data, how limited feature informativeness, class imbalance, and potential label or measurement noise jointly constrain learnability, even after standard rebalancing. A key implication is that reliable gas turbine fault detection will require richer, domain-informed feature engineering particularly temporal and condition-specific descriptors and possibly more expressive models, such as deep learning or hybrid physics-informed approaches. Future research should validate these insights on larger multi-plant datasets and systematically compare advanced feature-learning strategies and cost-sensitive optimization schemes empirically.]]>
