@misc{10788,
  abstract     = {{For process monitoring, an adequate data preprocessing is crucial to link accessible inline process data with offline measured target variables. Literature, however, does not provide systematic preprocessing strategies. The effects of five different preprocessing strategies on data from a Dielectric Spectroscopy system applied to the Viable Cell Density (VCD) of a mammalian cell cultivation were thus evaluated. Single-frequency measurements are typically used to model the VCD over the growth phase using linear regression or the Cole-Cole model and served as a reference. As multi-frequency measurement is promising to model the VCD beyond the growth phase using Partial Least Squares Regression (PLSR), we further aimed to determine, whether replacing linear regression by PLSR shows comparable modeling performance. All five preprocessing strategies led to comparable results. Exemplary, when using capacitance values at a frequency of 3347 kHz, linear regression resulted in a R2 of 0.90 and a standard deviation of 0.4 % on average. Both normalization techniques had the same positive effect on the results of PLSR. The order of smoothing and normalization was irrelevant for both regression methods. Comparing the results of linear regression and PLSR, the latter obtained on average 9 % better results. Therefore, we concluded that PLSR is preferable over linear regression and is potentially suitable to model the VCD beyond the growth phase, which is suggested to be investigated based on more data sets.}},
  author       = {{Ramm, Selina and Hernández Rodriguez, Tanja and Frahm, Björn and Pein-Hackelbusch, Miriam}},
  booktitle    = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}},
  editor       = {{Jasperneite, Jürgen and Wisniewski, Lukasz and Fung Man, Kim}},
  isbn         = {{978-1-6654-9314-7}},
  issn         = {{1935-4576}},
  keywords     = {{Spectroscopy, Smoothing methods, Systematics, Phase measurement, Linear regression, Data models, Dielectric measurement}},
  location     = {{Lemgo}},
  pages        = {{1--6}},
  publisher    = {{IEEE}},
  title        = {{{Systematic Preprocessing of Dielectric Spectroscopy Data and Estimating Viable Cell Densities}}},
  doi          = {{10.1109/INDIN51400.2023.10218012}},
  year         = {{2023}},
}

@inproceedings{4327,
  abstract     = {{In ever changing world, the industrial systems become more and more complex. Machine feedback in the form of alarms and notifications, due to its growing volume, becomes overwhelming for the operator. In addition, expectations in relation to system availability are growing as well. Therefore, there exists strong need for new solutions guaranteeing fast troubleshooting of problems that arise during system operation. The approach proposed in this study uses advantages of the Asset Administration Shell, machine learning, and human-machine interaction in order to create the assistance system which holistically addresses the issue of troubleshooting complex industrial systems.}},
  author       = {{Lang, Dorota and Wunderlich, Paul and Heinz, Mario and Wisniewski, Lukasz and Jasperneite, Jürgen and Niggemann, Oliver and Röcker, Carsten}},
  booktitle    = {{14th IEEE International Workshop on Factory Communication Systems (WFCS)}},
  keywords     = {{Maintenance engineering, Adaptation models, Machine learning, Data models, Standards, Software, Bayes methods}},
  location     = {{Imperia, Italy }},
  publisher    = {{IEEE}},
  title        = {{{Assistance System to Support Troubleshooting of Complex Industrial Systems}}},
  doi          = {{10.1109/WFCS.2018.8402380}},
  year         = {{2018}},
}