Simulation of Process Data with a Digital Model for the Purpose of Predictive Maintenance using OpenModelica

The demand for the acquisition of sensor data from manufacturing processes is increasing steadily. One emerging application that heavily relies on this data is data-driven predictive maintenance. However, research has shown that traditional machine learning models used for this purpose require the collection of large amounts of data under various conditions, which is a time-consuming and expensive process.Therefore, this paper presents an implementation of a digital model to increase the availability of process data for a conveyor belt system through simulation. First, the proposed model was built in the Open Modelica environment. Secondly, a Python interface was created for model interaction and validation purposes. To show the effectiveness of the model in simulating process data, the model is evaluated against physical data collected from the conveyor belt.