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Research Papers

Dynamic Model Validation Metric Based on Wavelet Thresholded Signals

[+] Author and Article Information
Andrew D. Atkinson

Department of Operational Sciences,
Air Force Institute of Technology,
Wright-Patterson AFB, OH 45433
e-mail: andrew.atkinson@afit.edu

Raymond R. Hill

Professor
Department of Operational Sciences,
Air Force Institute of Technology,
Wright-Patterson AFB, OH 45433
e-mail: raymond.hill@afit.edu

Joseph J. Pignatiello, Jr.

Professor
Department of Operational Sciences,
Air Force Institute of Technology,
Wright-Patterson AFB, OH 45433
e-mail: joseph.pignatiello@afit.edu

G. Geoffrey Vining

Professor
Department of Statistics,
Virginia Tech,
Blacksburg, VA 24061
e-mail: vining@vt.edu

Edward D. White

Professor
Department of Mathematics and Statistics,
Air Force Institute of Technology,
Wright-Patterson AFB, OH 45433
e-mail: edward.white@afit.edu

Eric Chicken

Professor
Department of Statistics,
Florida State University,
Tallahassee, FL 32306
e-mail: chicken@stat.fsu.edu

Manuscript received December 22, 2016; final manuscript received May 24, 2017; published online June 14, 2017. Editor: Ashley F. Emery.This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Verif. Valid. Uncert 2(2), 021002 (Jun 14, 2017) (10 pages) Paper No: VVUQ-16-1034; doi: 10.1115/1.4036965 History: Received December 22, 2016; Revised May 24, 2017

Model validation is a vital step in the simulation development process to ensure that a model is truly representative of the system that it is meant to model. One aspect of model validation that deserves special attention is when validation is required for the transient phase of a process. The transient phase may be characterized as the dynamic portion of a signal that exhibits nonstationary behavior. A specific concern associated with validating a model's transient phase is that the experimental system data are often contaminated with noise, due to the short duration and sharp variations in the data, thus hiding the underlying signal which models seek to replicate. This paper proposes a validation process that uses wavelet thresholding as an effective method for denoising the system and model data signals to properly validate the transient phase of a model. This paper utilizes wavelet thresholded signals to calculate a validation metric that incorporates shape, phase, and magnitude error. The paper compares this validation approach to an approach that uses wavelet decompositions to denoise the data signals. Finally, a simulation study and empirical data from an automobile crash study illustrates the advantages of our wavelet thresholding validation approach.

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References

Figures

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Fig. 1

Decomposition of signal S into approximation and details [26]

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Fig. 3

Decomposed signals (right-rear cross member)

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Fig. 4

Thresholded signals (RRCM)

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Fig. 5

Example data for follow-on study; system (blue) and model (red)

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