Low-Power Vibration-Based Predictive Maintenance for Industry 4.0 using Neural Networks: A Survey

• URL: http://arxiv.org/abs/2408.00516v1
• Date: Thu, 1 Aug 2024 12:46:37 GMT
• Title: Low-Power Vibration-Based Predictive Maintenance for Industry 4.0 using Neural Networks: A Survey
• Authors: Alexandru Vasilache, Sven Nitzsche, Daniel Floegel, Tobias Schuermann, Stefan von Dosky, Thomas Bierweiler, Marvin Mußler, Florian Kälber, Soeren Hohmann, Juergen Becker,
• Abstract summary: This paper investigates the potential of neural networks for low-power on-device computation of vibration sensor data for predictive maintenance. No satisfactory standard benchmark dataset exists for evaluating neural networks in predictive maintenance tasks. We highlight the need for future research on hardware implementations of neural networks for low-power predictive maintenance applications.
• Score: 33.08038317407649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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