The products of the DXQanalyze family collect machine and plant data and evaluate them. The data is visualized and can be evaluated historically, partly in real time, over long periods of time. Thanks to machine learning, the software is capable of detecting and predicting component wear and anomalies in production systems on the basis of rules and data. The remaining service life of system components can thus be predicted, and correlations between quality results and the machining process can also be established.
The award focuses on forward-looking solutions for industrial production. In a multi-stage selection process, DXQanalyze convinced the jury across all categories: "In Dürr's solution, it is impressive to see the level of maturity at which AI is already working and anomalies are being predicted," praised jury member Prof. Dr. Oliver Niggemann from the Institute for Automation Technology at Helmut Schmidt University / University of the Federal Armed Forces in Hamburg.