A multitude of high quality, high-resolution data is a cornerstone of the digital services associated with Industry 4.0. However, a great fraction of industrial machinery in use today features only a bare minimum of sensors and retrofitting new ones is expensive if possible at all. Instead, already existing sensors’ data streams could be utilized to virtually ‘measure’ new parameters. In this paper, a deep learning based virtual sensor for estimating a combustion parameter on a large gas engine using only the rotational speed as input is developed and evaluated. The evaluation focusses on the influence of data preprocessing compared to network type and structure regarding the estimation quality.
%0 Generic
%1 maschler2020learning
%A Maschler, Benjamin
%A Ganssloser, Sören
%A Hablizel, Andreas
%A Weyrich, Michael
%B 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '20 (Virtual Conference), 15-17 July 2020, Gulf of Naples, Italy
%D 2020
%K 2020ias 2020kiundmachinelearning ias
%R https://dx.doi.org/10.1016/j.procir.2021.03.115
%T Deep learning based soft sensors for industrial machinery
%X A multitude of high quality, high-resolution data is a cornerstone of the digital services associated with Industry 4.0. However, a great fraction of industrial machinery in use today features only a bare minimum of sensors and retrofitting new ones is expensive if possible at all. Instead, already existing sensors’ data streams could be utilized to virtually ‘measure’ new parameters. In this paper, a deep learning based virtual sensor for estimating a combustion parameter on a large gas engine using only the rotational speed as input is developed and evaluated. The evaluation focusses on the influence of data preprocessing compared to network type and structure regarding the estimation quality.
@conference{maschler2020learning,
abstract = {A multitude of high quality, high-resolution data is a cornerstone of the digital services associated with Industry 4.0. However, a great fraction of industrial machinery in use today features only a bare minimum of sensors and retrofitting new ones is expensive if possible at all. Instead, already existing sensors’ data streams could be utilized to virtually ‘measure’ new parameters. In this paper, a deep learning based virtual sensor for estimating a combustion parameter on a large gas engine using only the rotational speed as input is developed and evaluated. The evaluation focusses on the influence of data preprocessing compared to network type and structure regarding the estimation quality.},
added-at = {2021-01-18T14:39:25.000+0100},
author = {Maschler, Benjamin and Ganssloser, Sören and Hablizel, Andreas and Weyrich, Michael},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/262d27259e87743785ffd1e49b8d91e1b/taylansngerli},
booktitle = {14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '20 (Virtual Conference), 15-17 July 2020, Gulf of Naples, Italy},
doi = {https://dx.doi.org/10.1016/j.procir.2021.03.115},
interhash = {b42f9319022a79cc1931f9d81e40ea24},
intrahash = {62d27259e87743785ffd1e49b8d91e1b},
keywords = {2020ias 2020kiundmachinelearning ias},
timestamp = {2021-05-05T12:52:27.000+0200},
title = {Deep learning based soft sensors for industrial machinery},
year = 2020
}
