Interoperable system for automated extraction and identification of machine control data in brownfield production

Abstract

In recent years, Industrial Internet of Things (IIoT) respectively Industry 4.0 have become increasingly established and thus a widespread technology among companies. By means of data acquisition, processes can not only be made more sustainable and resource-efficient but through emerging technologies like fault detection or predictive maintenance, a higher Overall Equipment Effectiveness (OEE) can be achieved. As a result, the availability of the machine tools remains high and process chains will not be disturbed. However, many companies still operate in brownfield production sites with legacy machines and therefore with limited opportunities for machine data access and use in an economically viable manner. As a result, many advantages that would increase the OEE of machine tools cannot be used. To present a solution for retrofitting brownfield machines, a low-effort system was developed that extracts machine control signals from different machine data sources and automatically identifies them using a multi-stage algorithm. This algorithm consists of analytical rule bases built using expert knowledge and a machine learning model for classification. The best performing models were selected as machine learning models, which are a Residual Network, a Fully Convolutional Network, a Long-Short-Term Memory, and a Random Forest. With different datasets from two machine tools, the overall model was tested and was able to correctly identify the signals present with an accuracy of 86.92% to 98.93% on average. Using this approach, the identified signals are assigned to an information model linking them to the respective machine tool axes. As a result, brownfield machines are also made accessible for modern technologies such as predictive maintenance or a reduction in rejects as a result of error detection.