Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks

TL;DR

This paper introduces a neuromorphic, spike-based method for real-time vibration anomaly detection that is efficient, adaptable, and suitable for low-power edge devices, demonstrating state-of-the-art results and a practical implementation.

Contribution

It presents a novel end-to-end spike-based pipeline for vibration anomaly detection using balanced spiking neural networks, compatible with neuromorphic hardware, and demonstrates its effectiveness on real data.

Findings

Achieves state-of-the-art performance on public datasets

Operates in an online unsupervised manner

Successfully implemented on neuromorphic hardware

Abstract

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.