Paper Info
Title
Efficient Simulative Pass/Fail Characterization Applied to Automotive Power Steering
Abstract
Any component should optimally serve its application by providing exactly the right quantity of features and performances. This is called application fitness of a component. Application fitness can be assessed by simulating component models in an application model. Varying the component's performances may end up in a pass-or fail-behavior with regard to the application requirements. Characterizing the border between this pass and fails states is extremely helpful in the definition of the component's properties. With a number of component properties, this characterization problem gets complex. In this paper, we propose an approach for the planning of simulative experiments, to efficiently characterize this pass/fail border in n dimensions. Especially, smart sampling helps a lot to keep the simulation effort at bay, even if the pass or fail domain falls into a number of unconnected regions. The proposed approach is evaluated taking into account semiconductor components in an automotive electric power steering application. The smart sampling as proposed shows substantial improvements in the number of simulation runs while maintaining a comparable resolution at the border. 1
Year
DOI
Venue
2018
10.1109/AI4I.2018.8665717
2018 First International Conference on Artificial Intelligence for Industries (AI4I)
Keywords
Field
DocType
Aerospace electronics,Monte Carlo methods,Wheels,Power systems,Adaptation models,Estimation,Torque
Automotive engineering,Monte Carlo method,Torque,Semiconductor components,Computer science,Extremely Helpful,Electric power system,Power steering,Sampling (statistics),Automotive industry
Conference
ISBN
Citations 
PageRank 
978-1-5386-9209-7
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Jonas Stricker100.68
Benno Koeppl200.68
Andi Buzo38815.48
Jérôme Kirscher494.10
Linus Maurer593.44
Georg Pelz63212.24