Real-time fault diagnosis for gas turbine generator systems using extreme learning machine
Author:
Publisher
Elsevier BV
Subject
Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications
Reference34 articles.
1. A novel real-time fault diagnostic system for steam turbine generator set by using strata hierarchical artificial neural network;Yan;Energy Power Eng.,2009
2. An online condition monitoring and diagnosis system for feed rolls in the plate mill;Jeng;J. Manuf. Sci. Eng.,2002
3. The application of expert systems and neural networks to gas turbine prognostics and diagnostics;DePold;J. Eng. Gas Turbines Power,1999
4. A parallel distributed knowledge base system for turbine generator fault diagnosis;Wang;Artif. Intell. Eng.,1996
5. A semiautomatic approach to deriving turbine generator diagnostic knowledge;McArthur;IEEE Trans. Syst. Man Cybern. – Part C: Appl. Rev.,2007
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