Robust and real-time detection of faults on rotating machinery has become an ultimate objective for predictive maintenance in various industries. 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 neglected whilst only a few studies have been proposed during the last two decades, all of which were based on a conventional ML approach. One major reason is the lack of a benchmark dataset providing a large volume of both vibration and sound data over several working conditions for different machines and sensor locations. In this study, we address this need by presenting the new benchmark 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 overall. Then we draw the 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 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. With this study, the QU-DMBF dataset, the optimized source codes in PyTorch, and comparative evaluations are now publicly shared.

翻译：旋转机械故障的鲁棒实时检测已成为各行业预测性维护的最终目标。基于振动的深度学习（DL）方法已成为轴承故障检测的事实标准，因其在特定条件下可实现最优检测性能。尽管振动信号受到如此关注，声音信号的应用却长期被忽视——过去二十年仅有少量基于传统机器学习方法的研究。主要原因是缺乏涵盖不同机器、传感器位置及多种工况下大规模振动与声音数据的基准数据集。本研究通过提出新的基准数据集——卡塔尔大学双机轴承故障基准数据集（QU-DMBF），该数据集包含两台不同电机在总计1080种工况下运行的振动与声音数据，以填补这一需求。进而聚焦于振动故障检测因安装与运行条件差异导致的局限性。最后提出首个基于声音的故障检测深度学习方法，并在QU-DMBF数据集上对声音与振动方法进行对比评估。大量实验结果表明：基于声音的故障检测方法较振动方法具有显著更高的鲁棒性——其完全独立于传感器位置、成本效益高（无需传感器及维护），且能达到与振动方法相同的最优检测性能。本研究公开共享了QU-DMBF数据集、PyTorch优化源代码及对比评估结果。