Aiming at the insufficient deep network model training problem caused by limited rotary machine sensor signal samples， a fault diagnosis model combining improved residual convolutional auto-encoding network and class adaption method was proposed to deal with the data with small sample size. Firstly， paired samples were created by a small number of labeled source domain data and target domain data， and an improved one-dimensional residual convolutional auto-encoding network was designed to extract features from two types of original vibration signals with different distributions. Secondly， the Maximum Mean Discrepancy （MMD） was used to reduce the distribution difference， and the data space of the same fault category from two domains was mapped to a common feature space. Finally， the accurate fault diagnosis was realized. Experimental results show that the proposed model is able to effectively improve the fault diagnosis accuracy of the target domain vibration data with few labels under different working conditions compared with the fine-tuning and domain adaptation methods.