Hybrid Machine Learning Framework for Direction-Dependent Gearbox Fault Detection

Reliable quality control and fault diagnosis are essential for ensuring machine reliability and preventing unexpected failures. One of the critical machine components for which such a diagnosis enables failure-free, long-term exploitation is gearboxes. Conventional vibration-based monitoring often depends on expert interpretation of signal patterns and gear-mesh behaviour, which limits scalability and consistency. In this work, physics-informed machine-learning framework for binary gearbox health classification using engineered vibration features. Time and frequency domain descriptors capturing impulsiveness, gear-mesh spectral characteristics, and modulation effects were extracted from tri-axial acceleration signals. To account for direction-dependent dynamics, separate models were developed for left (RPM0) and right (RPM1) rotational conditions. We employ a unsupervised Isolation Forest trained exclusively on healthy data for anomaly detection, and a supervised Logistic Regression classifier trained on both healthy and faulty samples. Predefined decision thresholds were applied to ensure methodological transparency and minimize overfitting. Evaluation on independent test cases de