Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach

Author:

Sadough Vanini Z.N.,Khorasani K.,Meskin N.

Funder

NPRP

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference17 articles.

1. I.A.-D. Al-Zyoud, K. Khorasani, Detection of actuator faults using a dynamic neural network for the attitude control subsystem of a satellite. In: Procceding of International Joint Conference on Neural Networks, Montreal, Canada, 2005, pp. 1746–1751.

2. M. Ayoubi, Fault diagnosis with dynamic neural structure and application to a turbocharger, in: Proc. Int. Symp. Fault Detection, Supervision and Safety for technical Process SAFEPROCESS’94, Espoo, Finland, 1994, pp. 618–623.

3. A. Green, D. Allen, Artificial-intelligence for real-time diagnostics and prognostics of gas-turbine engines, in: 33rd AIAA/SME/SAE/ASEE Joint Propulsion Conference and Exhibit, Seattle, WA, USA, 1997, pp. 6–9.

4. K. Kanelopoulos, A. Stamatis, K. Mathioudakis, Incorporating neural-networks into gas-turbine performance diagnostics, in: ASME 97-GT-35, International Gas-turbine & Aero-engine Congress & Exhibition, Orlando, Florida, 1997.

5. T. Kobayashi, D.L. Simon, Application of a bank of kalman filters for aircraft engine fault diagnostics, in: Proceeding of the ASME Turbo Expo Conference, Atlanta, GA, USA, 2003, pp. 461–470.

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