Detecting Production Phases Based on Sensor Values using 1D-CNNs

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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Official URL: https://arxiv.org/abs/2004.14475


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
Subjects:DBIS Research > Publications
ID Code:1891
Deposited By: Herr Burkhard Hoppenstedt
BibTex Export:BibTeX
Deposited On:14 Nov 2022 14:55
Last Modified:14 Nov 2022 14:55

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