Title :
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
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;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7053420
