Comprehensive machine data acquisition through intelligent parameter identification and assignment

In today’s highly competitive manufacturing environment, process data monitoring continues to be of high priority, but often relies on modern communication interfaces being provided by PLC manufacturers. This paper proposes an alternative approach in which is acquired automatically from various models through available interfaces. Multiple Machine Learning algorithms are incorporated identify machine parameters, then assigned appropriate information models. All functionalities can a dedicated hardware module or as software modules IPCs. The proposed integrated into existing Industry 4.0 efforts accelerate digitalization challenging environments.