@article{(Open Science Index):https://publications.waset.org/pdf/10001213,
	  title     = {Fault Diagnosis of Nonlinear Systems Using Dynamic Neural Networks},
	  author    = {E. Sobhani-Tehrani and  K. Khorasani and  N. Meskin},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper presents a novel integrated hybrid
approach for fault diagnosis (FD) of nonlinear systems. Unlike most
FD techniques, the proposed solution simultaneously accomplishes
fault detection, isolation, and identification (FDII) within a unified
diagnostic module. At the core of this solution is a bank of adaptive
neural parameter estimators (NPE) associated with a set of singleparameter
fault models. The NPEs continuously estimate unknown
fault parameters (FP) that are indicators of faults in the system. Two
NPE structures including series-parallel and parallel are developed
with their exclusive set of desirable attributes. The parallel scheme is
extremely robust to measurement noise and possesses a simpler, yet
more solid, fault isolation logic. On the contrary, the series-parallel
scheme displays short FD delays and is robust to closed-loop system
transients due to changes in control commands. Finally, a fault
tolerant observer (FTO) is designed to extend the capability of the
NPEs to systems with partial-state measurement.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {2},
	  year      = {2015},
	  pages     = {539 - 549},
	  ee        = {https://publications.waset.org/pdf/10001213},
	  url   	= {https://publications.waset.org/vol/98},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 98, 2015},
	}