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.

翻译：工业4.0智能传感器的发展为低功耗预测性维护与状态监测提供了广阔机遇。然而，该领域的传统方法依赖于云端处理，导致能耗与存储成本高昂。本文研究了神经网络在振动传感器数据低功耗端侧计算用于预测性维护的潜力。通过分析数据集、数据预处理方法、网络架构及硬件实现方案，系统综述了脉冲神经网络（SNNs）与人工神经网络（ANNs）在振动预测性维护领域的相关文献。研究发现，目前缺乏用于评估预测性维护任务中神经网络的理想标准基准数据集。此外，频域变换是普遍采用的数据预处理手段。SNN主要采用浅层前馈架构，而ANN则探索了更广泛的模型类型与更深层的网络结构。最后，本文指出未来需重点关注低功耗预测性维护应用中神经网络的硬件实现方案研究，以及标准化基准数据集的构建。