Survey and evaluation of automated model generation techniques for high level modeling and high level fault modeling

• Main Authors: Xia, L., Farooq, M.U., Bell, I.M., Hussin, F.A., Malik, A.S.
• Format: Article
• Published: 2013
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891630810&doi=10.1007%2fs10836-013-5401-0&partnerID=40&md5=b6809552a6ec934a532b0dc1199dff8f; http://eprints.utp.edu.my/32705/

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. © 2013 Springer Science+Business Media New York.