Cloud-Edge Collaborative Method for Industrial Process Monitoring Based on Error-Triggered Dictionary Learning

Abstract

The development of cloud manufacturing enables data-driven process monitoring methods to reflect the real industrial process states accurately and timely. However, traditional process monitoring methods cannot update learned models once they are deployed to edge devices, which leads to model mismatch when confronted time-varying data. In addition, limited resources on the edge prevent it from deploying complex models. Therefore, this article proposes a novel cloud-edge collaborative process monitoring method. First, historical data of industrial processes are collected to establish a dictionary learning model and train the dictionary and classifier in the cloud. Then, the model is simplified and deployed to the edge. The edge layer monitors the process states, including fault detection and working condition recognition, and determines whether a model mismatch has occurred based on an error-triggered strategy. Both numerical simulation and industrial roasting process results verify the superiority of the proposed method.