An iterative adaptive online fault prognosis via hybrid fuzzy and importance sampling

Author

Al-Bayati, Ahmad Hussain ; Hong Wang

Author_Institution

Dept. of Comput. Sci., Kirkuk Univ., Kirkuk, Iraq

fYear

2014

Firstpage

4207

Lastpage

4212

Abstract

This paper discusses new directions of research to detect and diagnose Gaussian and non-Gaussian faults a new nonlinear observer (NOFS) based on Fuzzy and Sequential Important Sampling (FSIS) filter for each unknown states of the plant depending on the diagnosed. The idea based on expanding the size of freedom for the dynamic states of the observers. Therefore, NOFS has been designed and implemented to be robust nonlinear observer against the colored noise and non-Gaussian noise.

Keywords

adaptive systems; fault diagnosis; filtering theory; fuzzy set theory; importance sampling; iterative methods; nonlinear systems; observers; FSIS filter; Gaussian fault detection; Gaussian fault diagnosis; NOFS; colored noise; fuzzy and sequential important sampling filter; hybrid fuzzy-importance sampling; iterative adaptive online fault prognosis; nonGaussian fault detection; nonGaussian fault diagnosis; nonGaussian noise; robust nonlinear observer; Algorithm design and analysis; Approximation algorithms; Filtering algorithms; Heuristic algorithms; Observers; Probability distribution; Vectors; FSIS; FSIS hybrid Fuzzy and Important sequential Sampling; Filters based on hybrid Fuzzy and Important sequential Sampling Algorithm; NOFS; Nonlinear Fault Diagnose Observer; SIS; Sequential Important Sampling Algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2014 11th World Congress on

Type

conf

DOI

10.1109/WCICA.2014.7053420

Filename

7053420