Chapter 14 Identification of Lack of Knowledge Using Analytical Redundancy Applied to Structural Dynamic Systems Jakob Hartig, Florian Hoppe, Daniel Martin, Georg Staudter, Tugrul Öztürk, Reiner Anderl, Peter Groche, Peter F. Pelz, and Matthias Weigold 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. Keywords Interpretation of sensor data · Data-induced conflicts · Analytical redundancy · Lack of knowledge · Sensor error 14.1 Introduction On 14 March 2017, the ExoMars Schiaparelli Mars probe crashed, resulting in a total loss of the descent stage [1]. According to ESA, the cause was a defective orientation sensor that provided invalid values. Although conflicts with other data were detected, they were ignored. In the following two years, two fully manned Boeing 737 Max crashed, which in both cases led to the death of all passengers and the crews. According to the National Transportation Safety Committee, the cause was a malfunction of one of the angle of attack sensors which caused the control system to push the nose of the airplane down [2]. A difference between left and right angle of attack sensor of 20◦ was detected until the end of record. Highly automated systems rely on an increasing amount of information derived from data. However, the incidents show that the origin and context of data has not yet been sufficiently taken into account. Often data are assumed to be the true values and uncertainty or lack of knowledge is often assumed to be non-existent or is ignored in their generation process. If, on the other hand, data from several sources are interpreted simultaneously, they can induce conflicts. These in turn reveal J. Hartig · P. F. Pelz Chair of Fluid Systems, Technical University of Darmstadt, Darmstadt, Germany e-mail: jakob.hartig@fst.tu-darmstadt.de; peter.pelz@fst.tu-darmstadt.de F. Hoppe ( ) · D. Martin · P. Groche Institute for Production Engineering and Forming Machines, Technical University of Darmstadt, Darmstadt, Germany e-mail: hoppe@ptu.tu-darmstadt.de; daniel.martin@ptu.tu-darmstadt.de; groche@ptu.tu-darmstadt.de G. Staudter · R. Anderl Institute of Computer Integrated Design, Technical University of Darmstadt, Darmstadt, Germany e-mail: staudter@dik.tu-darmstadt.de; anderl@dik.tu-darmstadt.de T. Öztürk · M. Weigold Institute of Production Management, Technology and Machine Tools, Technical University of Darmstadt, Darmstadt, Germany e-mail: t.oeztuerk@ptw.tu-darmstadt.de; weigold@ptw.tu-darmstadt.de © The Society for Experimental Mechanics, Inc. 2020 Z. Mao (ed.), Model Validation and Uncertainty Quantification, Volume 3, Conference Proceedings of the Society for Experimental Mechanics Series, https://doi.org/10.1007/978-3-030-47638-0_14 131
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