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According to the problem of poor adaptability and low efficiency when traditional wavelet transform and BP neural network used for fault diagnosis, a new fault diagnosis algorithm for the fusion of lifting wavelet packet and improved BP neural network is proposed. Takes advantage of interpolating subdivision thinking, the prediction operator and update operator of lifting wavelet packet were designed, combining traditional wavelet packet algorithm and the principle of lifting mode, the lifting wavelet packet algorithm’s design was completed, and the algorithm was applied in extinction noise and energy feature extraction of fault signal. Use GA algorithm to optimize initial weights and thresholds of standard BP neural network algorithm, and use L-M algorithm to optimize the search of the standard BP neural network. Make use of the rolling experimental data provided by Case Western Reserve University, the new algorithm is applied to the experiment, the results show that:the new fault diagnosis algorithm has faster convergence, higher precision diagnostic effectiveness than the traditional BP neural network algorithm.