Artificial neural networks (ANN) constitute a powerful
class of nonlinear function approximate. ANN has been
widely used in pattern recognition, prediction and
classification. The application of wavelets in the fields of
engineering has grown rapidly in the past few years. To
improve the limitation of conventional applying traditional
fault diagnosis method on gas turbine, a novel diagnosis
approach integrating the wavelet transform with neural
network is proposed. It can overcome the problems caused
by local mimima of optimization. The model uses wavelets
as the activation functions in neural networks. The wavelet
basis function is assigned for each neuron of hidden layer
and each weight is determined by learning. With respect to
six kinds of typical and common fault based on thermo
dynamic parameter of gas turbine, use these data as sample
to train wavelet neural network, and then according the
output of network to determine the type of fault. The
experimental result show that the proposed gas turbine
fault diagnostic model based on wavelet neural networks
can diagnose the fault of gas turbine effectively. The
method can be generalized to other fault diagnosis.
