@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}, }