An Enhanced Hybrid Feature Integration and Boosted Learning Framework for Accurate Diagnostics of Automotive Clutch Systems

Abstract The increasing complexity of automotive clutch systems and the nonlinear interactions among thermal, mechanical, and operational parameters make accurate fault diagnosis a challenging task in modern vehicles. Most existing studies rely on conventional machine learning techniques or physics-based threshold methods, which often fail to capture cross-parameter dependencies and lack interpretability and adaptability under varying driving conditions. To address these limitations, this study proposes FusionCAT-X (Fusion Cognitive Attention-Infused Transformer with Boosted Ensemble Learning), a hybrid diagnostic framework that combines a Cognitive Attention-Infused Transformer (CAT) with a gradient boosted ensemble classifier to model complex feature interactions and improve clutch fault classification performance. The framework employs embedding-based representations to unify heterogeneous inputs and utilizes multi-head self-attention to dynamically learn interdependencies among clutch parameters, followed by ensemble-based decision refinement to improve classification boundary separation. The model is implemented using Python with TensorFlow and PyTorch for deep learning, along with NumPy, Pandas, and Scikit-learn for preprocessing and evaluation. Experiments are conducted on a publicly available Kaggle clutch failure dataset containing over 5,000 samples under diverse operating conditions. The proposed model achieves an accuracy of 97.90%, outperforming baseline methods such as SVM, KNN, and CatBoost by approximately 0.4% to 3.8%. The model outputs probabilistic confidence estimates; however, their calibration should be quantitatively evaluated. The results demonstrate that FusionCAT-X improves clutch fault classification by effectively learning nonlinear interactions among thermal, mechanical, and operational parameters.