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Neural Networks for Gas Turbine Fault Identification: Multilayer Perceptron or Radial Basis Network?

2012; De Gruyter; Volume: 29; Issue: 1 Linguagem: Inglês

10.1515/tjj-2012-0005

2191-0332

Igor Loboda, Yakov Feldshteyn, Volodymyr Ponomaryov,

Fault Detection and Control Systems

Efficiency of gas turbine condition monitoring systems depends on quality of diagnostic analysis at all its stages such as feature extraction (from raw input data), fault detection, fault identification, and prognosis. Fault identification algorithms based on the gas path analysis may be considered as an important and sophisticated component of these systems. These algorithms widely use pattern recognition techniques, mostly different artificial neural networks. In order to choose the best technique, the present paper compares two network types: a multilayer perceptron and a radial basis network. The first network is being commonly applied to recognize gas turbine faults. However, some studies note high recognition capabilities of the second network.