Planetary gearbox fault diagnostic method using acoustic emission sensors

In this study, a new acoustic emission (AE) sensor-based planetary gearbox (PGB) fault diagnosis method is presented. The method includes a heterodyne-based AE data acquisition system, empirical mode decomposition (EMD)-based AE signal analysis method, and computation of condition indicators (CIs) for PGB fault diagnosis. The heterodyne technique is hardware-implemented to downshift the sampling frequency of AE signals at a rate compatible to vibration analysis. The sampled AE signals are processed using EMD to extract PGB fault features and compute the CIs. The CIs are input into supervised learning algorithms for PGB fault diagnosis. The method is validated on a set of seeded localised faults on all gears: sun gear, planetary gear, and ring gear. The validation results have shown a promising PGB fault diagnostic performance using the presented method.