|
FAULT DIAGNOSIS OF AN INDUSTRIAL GAS TURBINE USING NEURO-FUZZY METHODS
Vasile. Palade1, Ron. J. Patton1, Faisel. J. Uppal1, Joseba Quevedo2, S. Daley3
1. The University of Hull, Control and Intelligent Systems Engineering,
Cottingham Road, HU6 7RX, Kingston upon Hull, United Kingdom,
E-mail: {V.Palade, R.J. Patton, F.J.Uppal}@hull.ac.uk
2. Automatic Control Department, Universidad Politécnica de Catalunya,
Campus de Terrassa, Rambla Sant Nebridi, 10. 08222 Terrassa, Spain,
E-mail: jquevedo@esaii.upc.es
3. Alstom Power Technology Centre, Cambridge Road, Whetstone,
Leicester, LE8 6LH, E-mail: Steve.Daley@power.alstom.com
The paper focuses on the application of neuro-fuzzy techniques in fault detection and isolation. The objective of this paper is to detect and isolate faults to an industrial gas turbine, with emphasis on faults occurred in the actuator part of the gas turbine. A neuro-fuzzy based learning and adaptation of TSK fuzzy models is used for residual generation, while for residual evaluation a neuro-fuzzy classifier for Mamdani models is used. The paper is concerned on how to obtain an interpretable fault classifier as well as interpretable models for residual generation.
Keywords: fault diagnosis, neural networks, fuzzy models, neuro-fuzzy, fault detection
Session slot T-We-A10: Neuro-Fuzzy Applications of Fault Diagnosis/Area code 7e : Fault Detection, Supervision and Safety of Technical Processes
 |
