  The advancements in smart sensors for Industry 4.0 offer ample opportunities
for low-powered predictive maintenance and condition monitoring. However,
traditional approaches in this field rely on processing in the cloud, which
incurs high costs in energy and storage. This paper investigates the potential
of neural networks for low-power on-device computation of vibration sensor data
for predictive maintenance. We review the literature on Spiking Neural Networks
(SNNs) and Artificial Neuronal Networks (ANNs) for vibration-based predictive
maintenance by analyzing datasets, data preprocessing, network architectures,
and hardware implementations. Our findings suggest that no satisfactory
standard benchmark dataset exists for evaluating neural networks in predictive
maintenance tasks. Furthermore frequency domain transformations are commonly
employed for preprocessing. SNNs mainly use shallow feed forward architectures,
whereas ANNs explore a wider range of models and deeper networks. Finally, we
highlight the need for future research on hardware implementations of neural
networks for low-power predictive maintenance applications and the development
of a standardized benchmark dataset.
