SFB 805 | transfer project T6
- Daniel Martin
- Julian Sinz
This chapter describes the various approaches to analyse, quantify and evaluate uncertainty along the phases of the product life cycle. It is based on the previous chapters that introduce a consistent classification of uncertainty and a holistic approach to master the uncertainty of technical systems in mechanical engineering. Here, the following topics are presented: the identification of uncertainty by modelling technical processes, the detection and handling of data-induced conflicts, the analysis, quantification and evaluation of model uncertainty as well as the representation and visualisation of uncertainty. The different approaches are discussed and demonstrated on exemplary technical systems.
Uncertainty affects all phases of the product life cycle of technical systems, from design and production to their usage, even beyond the phase boundaries. Its identification, analysis and representation are discussed in the previous chapter. Based on the gained knowledge, our specific approach on mastering uncertainty can be applied. These approaches follow common strategies that are described in the subsequent chapter, but require individual methods and technologies. In this chapter, first legal and technical aspects for mastering uncertainty are discussed. Then, techniques for product design of technical systems under uncertainty are presented. The propagation of uncertainty is analysed for particular examples of process chains. Finally, semi-active and active technical systems and their relation to uncertainty are discussed.
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.