Retrofitting cyber-physical production systems with radio-based sensors and ML

Abstract

Manufacturing companies usually isolate their production networks from other networks to ensure security against external attacks and to guarantee a fail-safe 24/7 operational service. However, these measures make it technically and organizationally complex to install new sensors or deploy new software in the production process. As a result, machine learning is only used to a limited extent in manufacturing, as these models require regular adaptations. To tackle this challenge, one possible solution is to install an additional network that is not connected to the production network. This network can be utilized for rapid prototyping of new sensors, advanced data analysis, or the deployment of machine learning models. One possible solution is to install a radio-based low-power, long-range network, having the property to capture data over large distances with only little power consumption. This paper examines the potential of retrofitting cyberphysical systems with such a network in combination with machine learning methods. The results are evaluated through three practical use cases: monitoring a workspace with a molding machine, monitoring the cycles of a washing machine, and predicting the daily consumption profile of a main water pipeline.