Deep learning for gear defect diagnosis and wear prediction in automotive systems

Abstract Gear failures in automotive systems are a leading cause of unplanned downtime, resulting in significant annual costs primarily driven by undetected mechanical wear and nanoparticle contamination. To address this, a novel deep learning framework is introduced that synergistically combines vibration signal analysis with scanning electron microscopy (SEM)-based nanoparticle quantification for enhanced predictive maintenance. The framework incorporates two key innovations: (1) GearDefectNet, a flipping-invariant hybrid convolutional and long short-term memory (CNN-LSTM) model for fault classification, and (2) NanoInsight, an adaptive watershed algorithm for automated nanoparticle analysis. When evaluated on real-world datasets, GearDefectNet achieved a fault classification accuracy of 98.2%, significantly outperforming traditional fast Fourier transform (FFT)-based methods (89.5%). NanoInsight demonstrated a measurement precision of ± 5 nm and identified a strong correlation between debris morphology and specific wear mechanisms (R² = 0.91). Validation on over 50 industrial gearboxes showed that the proposed pipeline reduces inspection time by 70% and enables the establishment of diagnostic thresholds for IoT-enabled monitoring. The framework is fully compliant with ISO 4406 and 15,243 standards, and supporting datasets and code are made available in adherence to FAIR data principles. These results demonstrate the framework’s efficacy for real-time gear defect detection and nanoparticle analysis in industrial applications.