A NEURAL NETWORK-BASED METHOD FOR GAS TURBINE BLADING FAULT DIAGNOSIS

摘要

In this paper artificial neural networks are used with promising results in a critical, and at the same time, very difficult problem concerning the diagnosis of gas turbine blading faults. Neural network-based fault diagnosis is treated as a pattern recognition problem, based on measurements and feature selection. Emphasis is given to the design of the appropriate neural network architecture and the selection of the appropriate measuring instruments, which are of critical importance for achieving good performance (high suc - cess rates and generalization capabilities).