Fog-supported Low-latency Monitoring of System Disruptions in Industry 4.0: A Federated Learning Approach

Industry 4.0 is based on machine learning and advanced digital technologies, such as Industrial-Internet-of-Things and Cyber-Physical-Production-Systems, to collect and process data coming from manufacturing systems. Thus, several industrial issues may be further investigated including, flows disruptions, machines’ breakdowns, quality crisis, and so on. In this context, traditional machine learning techniques require the data to be stored and processed in a central entity, e.g., a cloud server. However, these techniques are not suitable for all manufacturing use cases, due to the inaccessibility of private data such as resources’ localization in real time, which cannot be shared at the cloud level as they contain personal and sensitive information. Therefore, there is a critical need to go toward decentralized learning solutions to handle efficiently distributed private sub-datasets of manufacturing systems.

In this article, we design a new monitoring tool for system disruption related to the localization of mobile resources. Our tool may identify mobile resources (human operators) that are in unexpected locations, and hence has a high probability to disturb production planning. To do so, we use federated deep learning, as distributed learning technique, to build a prediction model of resources locations in manufacturing systems. Our prediction model is generated based on resources locations defined in the initial tasks schedule. Thus, system disruptions are detected, in real time, when comparing predicted locations to the real ones, that is collected through the IoT network. In addition, our monitoring tool is deployed at Fog computing level that provides local data processing support with low latency.

Furthermore, once a system disruption is detected, we develop a dynamic rescheduling module that assigns each task to the nearest available resource while improving the execution accuracy and reducing the execution delay. Therefore, we formulate an optimization problem of tasks rescheduling, before solving it using the meta-heuristic Tabu search. The numerical results show the efficiency of our schemes in terms of prediction accuracy when compared to other machine learning algorithms, in addition to their ability to detect and resolve system disruption in real time.