Bayesian edge analytics of machine process and health status in an IoT framework

Abstract

Using modern machine learning tools and embedded computing, a low-cost, integrated data acquisition platform is proposed in this work. Built on modern, open-source hardware and software, this platform enables high-quality sensor data acquisition and edge-based computation to facilitate machine health monitoring in an IoT framework. By leveraging proposed protocols for edge-based feature extraction, high-volume sensor data payloads are reduced in size to facilitate health monitoring and near real-time inference. The computational latency of this proposed methodology compares favorably to cloud-based solutions, where network transmission latency introduces significant variance in obtaining statistical features and model inference. A case study in tool wear analysis shows that CNC controller data may be used to contextualize accelerometer measurements and, in turn, facilitate training novelty detection and classification algorithms. These algorithms are then deployed to the edge device for near-real time inference.