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A novel fault diagnosis method for turbo-generator set based on fractal exponent theory and wavelet network is presented. When faults occur, they usually produce nonstationary vibration signals. The wavelet transform is used to localizes the characteristics of vibration signal in the time frequency domains and in a view of the inter relationship of wavelet transform between fractal theory, the whole and local fractal exponents obtained from wavelet transform coefficients as features are presented for extracting fault signals, which are inputted into radial basis function for fault pattern recognition. The improved Levenberg-Marquardt (LM) optimization technique is used to complete the network structure parameters. By means of choosing enough samples to train the fault diagnosis network and the information representing the faults is input into the trained wavelet network, and according to the output result the type of fault can be determined. The practical diagnosis for stator temperature fluctuation and rotor vibration demonstrates that the wavelet fractal network can provide an effective way to diagnosis faults for turbo-generator set in power system.