P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press Thiel, Christian Steidl, Carolin Henning, Bernd AMA Service GmbH Dynamic Time Warping Feature Extraction Masking Neural Networks The continuous refinement of sensor technologies enables the manufacturing industry to capture increasing amounts of data during the production process. As processes take time to complete, sensors register large amounts of time-series-like data for each product. In order to make this data usable, a feature extraction is mandatory. In this work, we discuss and evaluate different network architectures, input pre-processing and cost functions regarding, among other aspects, their suitability for time series of different lengths. 2019 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/15488 Thiel C, Steidl C, Henning B. P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press. In: AMA Service GmbH, ed. <i>20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019</i>. Von-Münchhausen-Str. 49, 31515 Wunstorf; 2019. doi:<a href="https://doi.org/10.5162/SENSOREN2019/P2.9">10.5162/SENSOREN2019/P2.9</a> eng info:eu-repo/semantics/altIdentifier/doi/10.5162/SENSOREN2019/P2.9 info:eu-repo/semantics/altIdentifier/isbn/978-3-9819376-0-2 info:eu-repo/semantics/closedAccess