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doi:10.22028/D291-36317
Titel: | Uncertainty-aware data pipeline of calibrated MEMS sensors used for machine learning |
VerfasserIn: | Dorst, Tanja Gruber, Maximilian Seeger, Benedikt Vedurmudi, Anupam Prasad Schneider, Tizian Eichstädt, Sascha Schütze, Andreas |
Sprache: | Englisch |
Titel: | Measurement: Sensors |
Bandnummer: | 22 |
Verlag/Plattform: | Elsevier |
Erscheinungsjahr: | 2022 |
Freie Schlagwörter: | Machine learnin Dynamic measurement uncertainty Interpolation Time series Predictive maintenance Low cost sensor network |
DDC-Sachgruppe: | 620 Ingenieurwissenschaften und Maschinenbau |
Dokumenttyp: | Journalartikel / Zeitschriftenartikel |
Abstract: | Sensors are a key element of recent Industry 4.0 developments and currently further sophisticated functionality is embedded into them, leading to smart sensors. In a typical “Factory of the Future” (FoF) scenario, several smart sensors and different data acquisition units (DAQs) will be used to monitor the same process, e.g. the wear of a critical component, in this paper an electromechanical cylinder (EMC). If the use of machine learning (ML) applications is of interest, data of all sensors and DAQs need to be brought together in a consistent way. To enable quality information of the obtained ML results, decisions should also take the measurement uncertainty into account. This contribution shows an ML pipeline for time series data of calibrated Micro-Electro-Mechanical Systems (MEMS) sensors. Data from a lifetime test of an EMC from multiple DAQs is integrated by alignment, (different schemes of) interpolation and careful handling of data defects to feed an automated ML toolbox. In addition, uncertainty of the raw data is obtained from calibration information and is evaluated in all steps of the data processing pipeline. The results for the lifetime prognosis of the EMC are evaluated in the light of “fitness for purpose”. |
DOI der Erstveröffentlichung: | 10.1016/j.measen.2022.100376 |
URL der Erstveröffentlichung: | https://www.sciencedirect.com/science/article/pii/S2665917422000101 |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-363178 hdl:20.500.11880/32994 http://dx.doi.org/10.22028/D291-36317 |
ISSN: | 2665-9174 |
Datum des Eintrags: | 1-Jun-2022 |
Drittmittel / Förderung: | EMPIR Met4FoF |
Fördernummer: | 17IND12 |
Fakultät: | NT - Naturwissenschaftlich- Technische Fakultät |
Fachrichtung: | NT - Systems Engineering |
Professur: | NT - Prof. Dr. Andreas Schütze |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
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1-s2.0-S2665917422000101-main.pdf | Artikel | 11,94 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons