Hartig, Jakob ; Hoppe, Florian ; Martin, Daniel ; Staudter, Georg ; Öztürk, Tuğrul ; Anderl, Reiner ; Groche, Peter ; Pelz, P. F. ; 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.

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