Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks
Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks
复制标题
使用平衡尖峰神经网络在线检测振动异常
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
G. Indiveri
中科院分区：
文献类型：
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作者：
N. Dennler;G. Haessig;M. Cartiglia;G. Indiveri
振动模式提供有关运行机器健康状态的有价值的信息，这些信息通常用于大型工业系统的预测性维护任务。然而，对于自动驾驶汽车、无人机或机器人等较小规模的应用来说，传统方法利用这些信息所需的大小、复杂性和电力预算方面的开销往往令人望而却步。在这里，我们提出了一种使用尖峰神经网络进行振动分析的神经形态方法，该方法可以应用于广泛的场景。我们提出了一种基于尖峰的端到端管道，能够从振动数据中检测系统异常，使用与模拟-数字神经形态电路兼容的构建块。这条管道以在线无人监督的方式运行，依赖于耳蜗模型、反馈适应和平衡的尖峰神经网络。实验结果表明，该方法在两个公开可用的数据集上取得了最好的性能或更好的效果。此外，我们还展示了一个在异步神经形态处理器设备上实现的工作概念验证。这项工作代表着朝着设计和实现用于在线振动监测的自主低功耗边缘计算设备迈出了重要的一步。
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 lowpower edge-computing devices for online vibration monitoring.