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

arXiv: https://arxiv.org/abs/2408.00516 · HTML: https://mlsanity.com/paper/2408.00516/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 81 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection](https://mlsanity.com/paper/1909.09347/) (2019)
- [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017)
- [TimeNet: Pre-trained deep recurrent neural network for time series classification](https://mlsanity.com/paper/1706.08838/) (2017)
- [A Survey of Predictive Maintenance: Systems, Purposes and Approaches](https://mlsanity.com/paper/1912.07383/) (2019)
- [An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG](https://mlsanity.com/paper/2009.11245/) (2020)
- [The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT](https://mlsanity.com/paper/2105.03371/) (2021)
- [Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks](https://mlsanity.com/paper/2106.00687/) (2021)

## Built on by (most cited first, 1 of 1)

- [Spiking Neural Networks for Low-Power Vibration-Based Predictive Maintenance](https://mlsanity.com/paper/2506.13416/) (2025)

## Abstract (arXiv)

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.