Comparing torsional and lateral vibration data for deep learning-based drive train gear diagnosis

Abstract

Deep learning-based fault diagnosis models have been demonstrated to recognise machine health conditions from vibration data. However, most related studies have focused on lateral vibration data, and mostly neglected torsional vibration data. Yet, torsional vibration data can provide an advantage in diagnosing gear faults. Torsional vibration is typically less noisy than lateral vibration data as it can be measured directly from the rotating components. To this end, this study presents a large gear fault dataset with artificial faults of varying severity acquired from a downscaled thruster test rig operated at a vast range of rotating speeds. The test rig was equipped with multiple torque transducers, rotary encoders and piezoelectric accelerometers. The lateral and torsional vibration data acquired with these sensors were evaluated with three popular convolutional neural networks in extensive ablation studies. An interpretability analysis was conducted based on amplitude spectra and Grad-CAM visualisations. The results demonstrate that torsional vibration can be an effective source of data for gear fault diagnosis. For example, the models diagnose the most difficult gear conditions using only one torque transducer more accurately than using three accelerometers mounted on the gear box. Furthermore, the highest accuracy in each ablation study related to experiments with combined lateral and torsional vibration data. In addition, the interpretability analysis showed that the lower frequencies had relatively higher amplitudes in torsional vibration than in lateral vibration. The interpretability analysis also indicates that the models reached higher classification accuracies with torsional vibration data due to the lower dominating frequencies. Overall, this study highlights the potential benefits of using torsional vibration data for deep learning-based fault diagnosis of gears.