Aiming at the working characteristics of variable conditions of an integrated transmission device of certain type of infantry fighting vehicle (IFV) in fantry fiohting vehide, a health prediction method based on transfer learning is proposed. Use the grey relational analysis (GRA) to extract the time series degradation features in the source and target domains as health indicators (HI) of each component, construct a one-dimensional series of health indicators, through the dynamic time warping (DTW) operation, the correlation between the characteristics of the target domain and the health indicators is obtained, extract the public degradation information of the source and target domains, construct a support vector regression (SVR) model for health prediction for health prediction, and take the transmission structure of the transmission device as an example to verify. The results show that the health prediction results based on transfer learning are more in line with actual health trends, and help the maintenance staff more accurately judge the health status of the transmission device.