Rolling bearings are important components in mechanical equipment,and their operational status is directly related to the operation of the equipment.When a failure occurs,it can disrupt the normal operation of the entire device, potentially leading to significant safety accidents.Therefore,predicting the remaining lifespan of these bearings is of crucial significance for equipment health management.This paper introduces a method for predicting the remaining lifespan of rolling bearings based on the transfer of an AELSTM model. Firstly,an autoencoder is utilized to automatically extract features from the raw vibration signals in the source domain.Subsequently,a two-layer LSTM model is constructed to predict the remaining lifespan.The AELSTM model is trained in the source domain and then fine-tuned with data from the target domain to adjust the model parameters.Finally,the adjusted model is used to predict the data in the target domain.By employing parameter sharing and fine-tuning methods,the training process of the model in the target domain is greatly simplified.The experimental results indicate that,under different operating conditions of the same bearing,the proposed model exhibited a reduction in root mean square error compared to four other transfer learning methods by 45.9%,58.9%,42.8%,and 83.8%respectively.In the case of different bearings under various operating conditions,the proposed model demonstrated reductions by 16.9%,18.9%,11.7%,and 8.9% respectively.