Streaming Workflows on Edge Devices to Process Sensor Data on a Smart Manufacturing Platform

Abstract

This paper describes a concept called Streaming Workflows that can collect data from sensors connected to edge devices and pass on the heavy load of computation to on-demand cloud services including Microsoft Azure, Amazon Web Services and Google Cloud Platform. The data streaming is done on the edge device while contextualization and modeling of the data are done with on-demand cloud resources. Many workflows using this edge-cloud architecture will be deployed on the cloud-based Smart Manufacturing (SM) PlatformTM developed by the Clean Energy Smart Manufacturing Innovation Institute (CESMII) at UCLA. Kepler workflows are used to orchestrate and manage the deployment of compute resources, the data transfer, data contextualization, modeling, and termination of the compute resources on a cloud platform. Test data in this study were from an aluminum rolling mill. The objective was to use operating data to predict exit temperature using an edge and Microsoft Azure architecture. This work addresses how to implement a run-time model-based control and optimization approach using Streaming Workflows for similar projects.