Data-Driven Fault Detection in Turbojet Engines: Benchmarking Temporal SVM Against State-of-the-Art Models

The detection of faults within turbojet engines represents an absolute necessity for operational safety and performance since failure to identify faults can lead to destructive system collapses. Real-time monitoring becomes possible through data-driven methods because they replace physical models which depend on system identification. The research presents T-SVM as a new framework for turbojet engine shaft speed fault detection through comparison with static SVM, XGBoost, Autoencoders and Long Short-Term Memory (LSTM) networks. The T-SVM algorithm uses Temporal Feature Enhancement (TFE) feature extraction technique with a 3-time-step delay to process experimental data containing an 18% fault rate. T-SVM demonstrates an accuracy level of 76.3% ± 23.5% and F1-score level of 0.51 ± 0.33 and AUC of 0.68 ± 0.27 during cross-validation with Bayesian hyperparameter optimization. This performance surpasses static SVM (59.4% ± 37.9% accuracy) by 16.9% but trails XGBoost (90.6% ± 1.3%), Autoencoders (90.7% ± 1.2%), and LSTM (90.7% ± 0.9%) due to their superior The T-SVM model shows high potential for real-time safety-critical usage because its true positive detection reaches 96.7% and its inference runs in under 0.01 seconds. Time-series analysis confirms how T-SVM identifies faulty sections when operating under varied conditions. The presented study demonstrates the trade-offs in fault detection based on data and recommends T-SVM as a powerful method which can be optimized by ensemble strategies to improve generalization capabilities.