Edge-cloud cooperation driven self-adaptive exception control method for the smart factory. (January 2022) - Wang, Wenbo; Hu, Tiantian; Gu, Jinan
Abstract: Production exceptions often occur due to the uncertainties of resources such as random machine broken, urgent production tasks. The timely identification and optimal control of the production exceptions are the core issues to ensure the reliable operation of smart manufacturing system. However, due to the evolution mechanism of uncertain factor is hard to describe, and the current cloud-based production analysis and decision-making mode often requires a long processing time. As a result, the extraction of abnormal events often delays and the optimal control decisions are hard to made. To meet this issue, an edge-cloud cooperation driven self-adaptive exception control method is proposed for the smart factory. Firstly, the advanced industrial Internet of things and embedded edge-computing technologies are applied to create an intelligent environment where the resources are made smart with the ability of self-sensing, self-analytics and self-optimization. Then, the fuzzy Bayesian network is used to extract the production exceptions and diagnose their causes, where fuzzy sets are used to describe the production status. After that, a self-adaptive production exception handling method is involved through an edge-cloud cooperation mechanism. The machines will adjust their production parameters at first, and then the horizontal collaboration among machines at same production level and vertical cooperation between different production levels will be used to deal with exceptions self-adaptively. Lastly, a case from casting post-processing system is used to demonstrate the proposed method, and results show that the proposed method can largely improve the production efficiency.