Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks
This work addresses the need for low-power, online vibration monitoring in applications like autonomous cars and drones, representing an incremental advance in neuromorphic computing for edge devices.
The authors tackled the problem of high overhead in vibration analysis for predictive maintenance by proposing a neuromorphic approach using balanced spiking neural networks, achieving state-of-the-art performance on two public datasets and demonstrating a proof-of-concept on a neuromorphic processor.
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.