# Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks

arXiv: https://arxiv.org/abs/2106.00687 · HTML: https://mlsanity.com/paper/2106.00687/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017)
- [Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition](https://mlsanity.com/paper/1911.08373/) (2019)
- [An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG](https://mlsanity.com/paper/2009.11245/) (2020)
- [Closed-loop spiking control on a neuromorphic processor implemented on the iCub](https://mlsanity.com/paper/2009.09081/) (2020)
- [An Automated Auto-encoder Correlation-based Health-Monitoring and Prognostic Method for Machine Bearings](https://mlsanity.com/paper/1703.06272/) (2017)

## Built on by (most cited first, 3 of 3)

- [Low-Power Vibration-Based Predictive Maintenance for Industry 4.0 using Neural Networks: A Survey](https://mlsanity.com/paper/2408.00516/) (2024)
- [Spiking Neural Networks for Low-Power Vibration-Based Predictive Maintenance](https://mlsanity.com/paper/2506.13416/) (2025)
- [Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring](https://mlsanity.com/paper/2608.18341/) (2026)

## Abstract (arXiv)

Vibration patterns yield valuable information about the health state of a running machine, which is commonly exploited in predictive maintenance tasks for large industrial systems. However, the overhead, in terms of size, complexity and power budget, required by classical methods to exploit this information is often prohibitive for smaller-scale applications such as autonomous cars, drones or robotics. Here we propose a neuromorphic approach to perform vibration analysis using spiking neural networks that can be applied to a wide range of scenarios. We present a spike-based end-to-end pipeline able to detect system anomalies from vibration data, using building blocks that are compatible with analog-digital neuromorphic circuits. This pipeline operates in an online unsupervised fashion, and relies on a cochlea model, on feedback adaptation and on a balanced spiking neural network. We show that the proposed method achieves state-of-the-art performance or better against two publicly available data sets. Further, we demonstrate a working proof-of-concept implemented on an asynchronous neuromorphic processor device. This work represents a significant step towards the design and implementation of autonomous low-power edge-computing devices for online vibration monitoring.