Fault identification through the use of Artificial Neural Networks has become very popular recently. Probabilistic Neural Networks (PNN) is one of the architectures, which have mostly been investigated for gas turbine diagnostics. In this paper, the influence of parameters related to the structure and training on the diagnostic performance of a probabilistic Neural Network (PNN), is investigated. In particular, the parametric investigation examines the effect of the training set on the diagnostic performance of a PNN. The effect of noise level was also examined and found to be important. Another parameter examined is the severity of a fault, which was found to affect seriously the performance of the diagnostic PNN. Other parameters also examined are the effect of the operating conditions as well as the considered output parameters of the network. Guidelines useful for setting up this type of network, are derived.
- International Gas Turbine Institute
A Parametric Investigation of the Diagnostic Ability of Probabilistic Neural Networks on Turbofan Engines
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Romessis, C, Stamatis, A, & Mathioudakis, K. "A Parametric Investigation of the Diagnostic Ability of Probabilistic Neural Networks on Turbofan Engines." Proceedings of the ASME Turbo Expo 2001: Power for Land, Sea, and Air. Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award. New Orleans, Louisiana, USA. June 4–7, 2001. V004T04A004. ASME. https://doi.org/10.1115/2001-GT-0011
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