for Simplified Access of Existing Low Level Process Control and Sensor Data for Structured Stream Analytics
for Automated and Prioritized Transfer of Successful Analytics Services and Optimization Measures
for Data Driven Training of Digital Twins under Unified View on Systems, Processes & Products
Commissioning of automated production systems usually follows standardized, stability-oriented procedures. Hence, the resulting performance is limited and the potential to increase productivity often exceeds 10%. However, relevant factors (e. g. system parameters, environmental conditions) influencing the process are often unknown.
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Production processes of identical or similar lines, cells or machines often run in parallel, at the same or at a different location. Alternatively, equal processes are operated using similar systems from different manufacturers.
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Environmental protection and sustainability are becoming increasingly important for consumers. Eco-friendly manufactured goods, therefore, drive the direction of production companies.
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Maintenance is usually planned preventively or after fixed intervals. Critical process deviations not detected in time, resulting in unexpected system failures.
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The High-Tech Startup Fine|alyze is a Spinoff Initiative from the Institute of Mechatronic Systems at the Gottfried Wilhelm Leibniz University Hannover.
Emerged from the Research Group for Integrated Systems and Machine Learning, it is now our Mission to Optimize Automated Production with Application of our Innovative and High Performant Digitalization and Data Analytics solutions.
Our Customers benefit from our Broad Technological and Methodical Expertise, Agile and Solution-Oriented Approaches as well as Extensive Experience from Research and Transfer Projects.
more info: Niedersachsenmetall Industrie 4.0 Kongress
November 14, 2019
more info: Deutsche Management Akademie Niedersachsen
October 29, 2019
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 761349.
This project receives funding by the German Federal Environmental Foundation (DBU - Deutsche Bundesstiftung Umwelt) under the Green Start-up special program.
