Exploring Sound Versus Vibration for Robust Fault Detection on Rotating Machinery

Robust and real-time detection of faults has become an ultimate objective for predictive maintenance on rotating machinery. Vibration-based deep learning (DL) methodologies have become the de facto standard for bearing fault detection as they can produce state-of-the-art detection performances under certain conditions. Despite such particular focus on the vibration signal, the utilization of sound, on the other hand, has been widely neglected. As a result, no large-scale benchmark motor fault dataset exists with both sound and vibration data. The novel and significant contributions of this study can be summarized as follows. This study presents and publically shares the Qatar University dual-machine bearing fault benchmark dataset (QU-DMBF), which encapsulates sound and vibration data from two different motors operating under 1080 working conditions. Then, we focus on the major limitations and drawbacks of vibration-based fault detection due to numerous installation and operational conditions. Finally, we propose the first DL approach for sound-based fault detection and perform comparative evaluations between the sound and vibration signals over the QU-DMBF dataset. A wide range of experimental results shows that the sound-based fault detection method is significantly more robust than its vibration-based counterpart, as it is entirely independent of the sensor location, cost-effective (requiring no sensor and sensor maintenance), and can achieve the same level of the best detection performance by its vibration-based counterpart. This study publicly shares the QU-DMBF dataset, the optimized source codes in PyTorch, and comparative evaluations with the research community.