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Identification of lack of knowledge using analytical redundancy applied to structural dynamic systems

Hartig, Jakob and Hoppe, Florian and Martin, Daniel and Staudter, Georg and Öztürk, Tuğrul and Anderl, Reiner and Groche, Peter and Pelz, P. F. and Weigold, Matthias Mao, Zhu (ed.) (2020):
Identification of lack of knowledge using analytical redundancy applied to structural dynamic systems.
In: Conference Proceedings of the Society for Experimental Mechanics Series, In: Model Validation and Uncertainty Quantification ; Vol. 3, pp. 131-138,
Cham, Springer International Publishing, Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020, ISBN 978-3-030-48778-2,
DOI: 10.1007/978-3-030-47638-0_14,
[Conference or Workshop Item]

Abstract

Reliability of sensor information in today’s highly automated systems is crucial. Neglected and not quantifiable uncertainties lead to lack of knowledge which results in erroneous interpretation of sensor data. Physical redundancy is an often-used approach to reduce the impact of lack of knowledge but in many cases is infeasible and gives no absolute certainty about which sensors and models to trust. However, structural models can link spatially distributed sensors to create analytical redundancy. By using existing sensor data and models, analytical redundancy comes with the benefits of unchanged structural behavior and cost efficiency. The detection of conflicting data using analytical redundancy reveals lack of knowledge, e.g. in sensors or models, and supports the inference from conflict to cause. We present an approach to enforce analytical redundancy by using an information model of the technical system formalizing sensors, physical models and the corresponding uncertainty in a unified framework. This allows for continuous validation of models and the verification of sensor data. This approach is applied to a structural dynamic system with various sensors based on an aircraft landing gear system.

Item Type: Conference or Workshop Item
Erschienen: 2020
Editors: Mao, Zhu
Creators: Hartig, Jakob and Hoppe, Florian and Martin, Daniel and Staudter, Georg and Öztürk, Tuğrul and Anderl, Reiner and Groche, Peter and Pelz, P. F. and Weigold, Matthias
Title: Identification of lack of knowledge using analytical redundancy applied to structural dynamic systems
Language: English
Abstract:

Reliability of sensor information in today’s highly automated systems is crucial. Neglected and not quantifiable uncertainties lead to lack of knowledge which results in erroneous interpretation of sensor data. Physical redundancy is an often-used approach to reduce the impact of lack of knowledge but in many cases is infeasible and gives no absolute certainty about which sensors and models to trust. However, structural models can link spatially distributed sensors to create analytical redundancy. By using existing sensor data and models, analytical redundancy comes with the benefits of unchanged structural behavior and cost efficiency. The detection of conflicting data using analytical redundancy reveals lack of knowledge, e.g. in sensors or models, and supports the inference from conflict to cause. We present an approach to enforce analytical redundancy by using an information model of the technical system formalizing sensors, physical models and the corresponding uncertainty in a unified framework. This allows for continuous validation of models and the verification of sensor data. This approach is applied to a structural dynamic system with various sensors based on an aircraft landing gear system.

Title of Book: Model Validation and Uncertainty Quantification ; Vol. 3
Series Name: Conference Proceedings of the Society for Experimental Mechanics Series
Place of Publication: Cham
Publisher: Springer International Publishing
ISBN: 978-3-030-48778-2
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institut für Produktionstechnik und Umformmaschinen (PtU)
16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW)
16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > Machine tools and Components
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 805: Control of Uncertainty in Load-Carrying Structures in Mechanical Engineering
Event Title: Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020
Date Deposited: 15 Apr 2020 05:14
DOI: 10.1007/978-3-030-47638-0_14
Official URL: https://link.springer.com/book/10.1007%2F978-3-030-47638-0
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