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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, a hybrid 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 an 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 demonstrates that direction-specific
modelling combined with physically interpretable features enables robust gearbox fault detection. The proposed
framework provides a reproducible and industrially applicable strategy for automated condition monitoring. Such
an approach will provide precise solutions for early fault detection, predictive maintenance scheduling, and real-
time performance optimization of gearboxes and machinery systems.
