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Design of Experiments

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Nonlinear System Identification

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Abstract

This chapter is devoted to the application of various design of experiment strategies. First, some fundamental questions are addressed in simulation studies, such as “In what order should measurements be carried out?” and “Should corners of the input space be measured?” Then, HILOMOT-DoE, an active learning strategy based on the HILOMOT algorithm, is applied to a structural health monitoring application. In a second application example, HILOMOT-DoE is utilized for efficient combustion engine measurement at a test stand. With this approach, half of the measurement time could be saved compared to conventional DoE strategies, still achieving comparable model quality. Finally, the excitation signal generator proposed in Chap. 19 is very successfully applied to a common rail fuel injection system. The benefits of such a generator-based DoE approach are demonstrated.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Variable-length_intake_manifold

  2. 2.

    This is a special kind of optimal block design that is beyond the scope of this book. It was offered by the software and achieved good results in the past.

  3. 3.

    Because the amount of data is much higher for the global DoE than for each local DoE, the degree of the polynomial is chosen higher as well.

  4. 4.

    Many thanks to my research assistant Tim O. Heinz and my external Ph.D. student Mark Schillinger, Bosch Engineering, for providing me with the above application example.

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  1. University of Siegen, Netphen, Germany

    Oliver Nelles

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  1. Oliver Nelles
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Nelles, O. (2020). Design of Experiments. In: Nonlinear System Identification. Springer, Cham. https://doi.org/10.1007/978-3-030-47439-3_26

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