Robust planetary gearbox fault diagnosis through time–frequency analysis and transfer learning

Abstract

Gearboxes are essential mechanical components for power transmission. Among them, planetary gearboxes stand out for their compact design and high reduction ratio, making them particularly advantageous in various sectors such as energy generation, transportation, and robotics. Like all mechanical components under load, they are susceptible to different types of degradation, including wear, cracks, chipping, and even broken teeth. Such defects can negatively impact transmission quality, potentially leading to system shutdowns and endangering operators. This necessitates an autonomous monitoring solution that can respond in real-time. This paper presents a robust monitoring methodology for diagnosing faults in planetary gearboxes. The approach begins with filtering the monitoring signals using Variational Mode Decomposition (VMD). The filtered signal is then transformed into a time–frequency image using Wavelet Transform (WT). These images are subsequently used to train a transfer learning network. To ensure the robustness of the proposed intelligent solution, a data augmentation step is included to address issues of data scarcity and imbalanced datasets. Experimental validation demonstrates the effectiveness of the proposed methodology under different operating conditions. • Autonomous, intelligent fault diagnosis methodology for planetary gearboxes. • Signal filtering using variational mode decomposition for noisy environments. • Solutions to data scarcity and dataset imbalance in intelligent diagnosis.