Abstract: To solve the problem that the measure data is high in dimensionality, nonlinear and multistage in the process of degradation. A lifetime estimation model based on nonlinear data fusion and multistage degradation is proposed. Firstly, the degradation indicator of equipment was constructed by fuse data fused by AutoEncoder technology based on neural network theory. Then the breakpoint of the multi-stage degradation process of the equipment was extracted by CUSUM breakpoint analysis method. At last, the multi-stage Wiener degradation model is constructed to predict the lifetime. The proposed method is validated by aero-engine condition monitoring data. The results show that compared with the PCA method and the single-stage Wiener degradation model, the average error of remaining useful lifetime prediction is 0.254 5, lower than the traditional methods. The verification results show that the multi-stage lifetime estimation model based on nonlinear data fusion has good robustness and more accurate.