Hoppenstedt, Burkhard and Reichert, Manfred and El-Khawaga, Ghada and Winter, Karl-Michael and Pryss, Rüdiger (2020) Detecting Production Phases Based on Sensor Values using 1D-CNNs. arXiv .
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Abstract
In the context of Industry 4.0, the knowledge extraction from sensor information plays an important role. Often, information gathered from sensor values reveals meaningful insights for production levels, such as anomalies or machine states. In our use case, we identify production phases through the inspection of sensor values with the help of convolutional neural networks. The data set stems from a tempering furnace used for metal heat treating. Our supervised learning approach unveils a promising accuracy for the chosen neural network that was used for the detection of production phases. We consider solutions like shown in this work as salient pillars in the field of predictive maintenance.
Item Type: | Article |
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Subjects: | DBIS Research > Publications |
Divisions: | Faculty of Engineering, Electronics and Computer Science > Institute of Databases and Informations Systems Faculty of Engineering, Electronics and Computer Science > Institute of Databases and Informations Systems > DBIS Research and Teaching Faculty of Engineering, Electronics and Computer Science > Institute of Databases and Informations Systems > DBIS Research and Teaching > DBIS Research > Publications |
Depositing User: | Herr Burkhard Hoppenstedt |
Date Deposited: | 14 Nov 2022 14:55 |
Last Modified: | 14 Nov 2022 14:55 |
URI: | http://dbis.eprints.uni-ulm.de/id/eprint/1891 |