Monday  |  Cottonwood  |  01:50 PM–02:10 PM

#8118, Identification of Lack of Knowledge using Analytical Redundancy Applied to Structural Dynamic Systems

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 and supports the inference from conflict to cause, e.g. sensors, models.
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.

Jakob Hartig   TU Darmstadt
Florian Hoppe   TU Darmstadt
Georg Staudter   TU Darmstadt
Tugrul Öztürk   TU Darmstadt
Reiner Anderl   TU Darmstadt
Peter Groche   TU Darmstadt
Peter Pelz   TU Darmstadt
Matthias Weigold   TU Darmstadt