A study on intelligence fault diagnosis system of turbine machine

This paper presents an intelligent methodology for diagnostics of incipient faults in turbine machine. A fault diagnosis system is developed for turbine machine. In this system, wavelet transform techniques are used in combination with function approximation model to extract fault features used in the diagnosis of turbine machine faults. The neural networks is constituted. The main contributions of this paper are two aspects. A improvement method based on nonlinear adaptive algorithm has been developed for excitation function approximation of neural networks. In order to perform diagnosis using intelligent system, a preprocessing of singularity fault signal is required. The second contribution is the development of a neural networks classifier for identification of fault. The developed system is scalable to different turbine machine and it has been successfully demonstrated with a turbine generator unit.