  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.
