Unified Adaptive Deep Classification For Industrial Real-Time Situation Awareness In Edge Environment

The rapidly growing information technologies such as cloud computing, edge computing, artificial intelligence offer cutting-edge technical means for virtual reality of industrial manufacturing process. An efficient and accurate method for situation awareness (SA) to monitor the process of marginal layer plays an essential role to enhance the safety of industrial equipment. In this study, a manufacturing life cycle is described and characterized as operation and remote controlling of industrial machineries. The edge servers are first employed to track physical devices. Then a novel unified adaptive deep classification framework for SA with semi-supervised classification method is developed to find unknown patterns in a huge amount of industrial data. Furthermore, a robust model based on the selection of adaptive local neighbor is proposed to find out an optimal weight and high similarity of neighbors. The combination of the proposed framework and model can realize real-time SA of modern manufacturing the execution system. Plentiful experiments have been conducted to demonstrate the high accuracy and reliability of the proposed framework.