Lehrstuhl für Technologien und Management der Digitalen Transformation
Herr Univ.-Prof. Dr.-Ing. Tobias Meisen
Industrial Sensory Data Analytics
One consequence of the digital transformation for the manufacturing industry is the networking of production machines and the collection of sensor data for the control and monitoring of machines during their operation. The collected sensor data holds great potential for data-driven analyses, for example in scenarios such as predictive maintenance or predictive quality.
Our application-oriented research for the predictive analysis of industrial sensor data uses methods from the field of signal processing and combines them with modern procedures from the areas of machine learning and deep learning. The focus is on analyses
- for classification of errors from historical data,
- for predicting error patterns based on anomalies in the sensor data,
- for forecasting signal characteristics for predictive control of machines and
- to developing AI-based soft sensors for the reconstruction of physical sensors in a virtual software sensor.
Contact
Interested in this research
Would you like to write a thesis in this research area? Then look here for open topics or contact meyes{at}uni-wuppertal.de
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Selected relevant publications
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Artificial Intelligence in Automotive Production
Mobility in a Globalised World 2018,
22:308--324
2019
DOI: 10.20378/irbo-54827
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A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data
Procedia Manufacturing,
34:789--797
2019
ISSN: 2351-9789
Note: 47th SME North American Manufacturing Research Conference, NAMRC 47, Pennsylvania, USA. |
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Similarity Search of Bounded TIDASETs within Large Time Interval Databases
2015 International Conference on Computational Science and Computational Intelligence (CSCI),
:24--29
2016
DOI: 10.1109/CSCI.2015.36
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TIDAQL - A Query Language Enabling on-Line Analytical Processing of Time Interval Data
Proceedings of the 17th International Conference on Enterprise Information Systems,
:54--66
2015
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Querying Time Interval Data
ICEIS 2015,
2015
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Bitmap-Based On-line Analytical Processing of Time Interval Data
2015 12th International Conference on Information Technology - New Generations,
:20--26
2015
DOI: 10.1109/ITNG.2015.9
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Modeling and Processing of Time Interval Data for Data-driven Decision Support
2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC),
:2946--2953
2014
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