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On System Identification for Accelerated Destructive Degradation Testing of Nonlinear Dynamic Systems

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Book cover Statistical Modeling for Degradation Data

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

Accelerated destructive degradation testing is considered with the objective of reproducing high fatigue incidents for a severely nonlinear system in a lab environment. In the lab, a test specimen is mounted on servo hydraulic actuators which are then used to induce the same response in the system as was measured in field tests. Finding the inputs to the actuators that accurately induce the measured response in the system is crucial to the integrity of the testing procedure. The problem is an inverse problem, and often exhibits ill-posed characteristics. To this end a new method for system identification from time series data is developed and is shown to outperform current methods such as different variants of NARX and Hammerstein-Wiener models. From the results obtained it is concluded that an alternative method of data generation for accelerated destructive degradation on severely nonlinear systems in a lab context is required. Three methods are developed and tested on simulated data and it is shown that a prototype bootstrapping strategy is superior: using 400,000 data points generated by this strategy the input signals were predicted with mean square errors of 5.08e-4.

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Notes

  1. 1.

    Method 514.6 Annex A.

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Author information

Authors and Affiliations

  1. Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria, South Africa

    Jacq Crous, Daniel Nicolas Wilke, Schalk Kok & Stephan Heyns

  2. School of Social Work & Department of Biostatistics, University of North Carolina, 27599, Chapel Hill, NC, USA

    Ding-Geng (Din) Chen

  3. Department of Statistics, Univeristy of Pretoria, Pretoria, South Africa

    Ding-Geng (Din) Chen

Authors
  1. Jacq Crous
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  2. Daniel Nicolas Wilke
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  3. Schalk Kok
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  4. Ding-Geng (Din) Chen
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  5. Stephan Heyns
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Corresponding author

Correspondence to Jacq Crous .

Editor information

Editors and Affiliations

  1. School of Social Work & Department of Biostatistics, Department of Statistics, University of North Carolina, University of Pretoria, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

  2. Department of Mathematical Sciences, University of South Dakota, Vermillion, South Dakota, USA

    Yuhlong Lio

  3. Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA

    Hon Keung Tony Ng

  4. Department of Statistics, Tamkang University, New Taipei City, Taiwan

    Tzong-Ru Tsai

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© 2017 Springer Nature Singapore Pte Ltd.

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Crous, J., Wilke, D.N., Kok, S., (Din) Chen, DG., Heyns, S. (2017). On System Identification for Accelerated Destructive Degradation Testing of Nonlinear Dynamic Systems. In: Chen, DG., Lio, Y., Ng, H., Tsai, TR. (eds) Statistical Modeling for Degradation Data. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-5194-4_17

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