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Survey and Evaluation of Automated Model Generation Techniques for High Level Modeling and High Level Fault Modeling

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Abstract

It is known that automated model generation (AMG) techniques for linear systems are sufficiently mature to handle linear systems during high level modeling (HLM). Other AMG techniques have been developed for various levels of nonlinear behavior and to focus on specific issues such as high level fault modeling (HLFM). However, no single nonlinear AMG technique exists which can be confidently adapted for any nonlinear system. In this paper, a survey on AMG techniques over the last two decades is conducted. The techniques are classified into two main areas: system identification (SI) based AMG and model order reduction (MOR) based AMG. Overall, the survey reveals that more advanced research for AMG techniques is required to handle strongly nonlinear systems during HLFM.

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Acknowledgment

This work was supported by the Fundamental Research Grant Scheme (Ref: FRGS 2/2010/TK/UTP/03/8, Ministry of High Education (MOHE), Malaysia.

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  1. Center for Intelligent Signal & Imaging Research, Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Tronoh, 31750, Perak, Malaysia

    Likun Xia, Muhammad Umer Farooq, Fawnizu Azmadi Hussin & Aamir Saeed Malik

  2. Department of Engineering, University of Hull, Hull, HU6 7RX, UK

    Ian M. Bell

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  1. Likun Xia
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  2. Muhammad Umer Farooq
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Correspondence to Likun Xia.

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Xia, L., Farooq, M.U., Bell, I.M. et al. Survey and Evaluation of Automated Model Generation Techniques for High Level Modeling and High Level Fault Modeling. J Electron Test 29, 861–877 (2013). https://doi.org/10.1007/s10836-013-5401-0

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