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The representative characteristics of large-scale rotating machine in operation are non-stationary and nonlinear, and also influenced by sudden and accidental factors, thus the difficulty in condition monitoring and fault prediction based on vibration signal analysis is great. Artificial neural networks, which perform a nonlinear mapping between inputs and outputs, are widely used in fault prediction, but easy to fall into local optimal solution and converge with slow speed or even diverge. In this paper, aimed at this problem, the dynamic prediction model is studied,in which back propagation(BP) algorithm coupled with genetic algorithm(GA) will be used to train and optimize the networks. BP of ANN has been recognized as a powerful mapping approach to model extremely complex nonlinear process while GA for global search ability was used in various diverse optimization systems. Owing to complementary advantages of both merged, the accuracy of the GA-BP networks is improved significantly. The final goal is to improve the performance of GA-BP network in predicting faulty and estimating residual life for rotating machinery. Ultimately, verification of the optimization was showed at the bearing wear data from the power fan of a environmental simulation test system.