Abstract:From the perspective of flow degradation trend, a life prediction method based on Adaptive Networkbased Fuzzy Inference System (ANFIS) is proposed. Firstly, the modified ensemble empirical mode decomposition (MEEMD) method is used to perform multiscale reconstruction and noise reduction on the vibration data of accelerated degradation test. The kurtosis, meansquare frequency, wavelet packet energy of the reconstructed signal are extracted, which together with the signals of torque, rotation speed and pressure are used as the characteristics of performance degradation of outertooth gear pump. Then, kernel principal component analysis (KPCA) method is used to perform the multiple feature fusion. Furthermore, the establishment and analysis of the degradation evaluation indices of the outertooth gear pump are realized. The degradation evaluation indices and flow signals are used to train the ANFIS model, and the remaining life prediction model of the gear pump is obtained. In order to further verify the effectiveness of the algorithm, the gear pump remaining life prediction model is compared with liner regression model and cubic exponential prediction model. Finally, based on the Monte Carlo sample expansion method, the reliability evaluation of the outertooth gear pump is achieved. The results show that the prediction error between the result of the proposed method and the actual threshold is about 8%, the proposed method can accurately evaluate the life of the outertooth gear pumps.