Abstract: Reliability is an important indicator of the quality and stability of remanufactured parts. Compared with new product parts,there are many reliability factors affecting remanufactured parts,complex mechanism relationships,and high residual value of blanks,which makes it difficult to carry out a large number of reliability tests,resulting in a small sample size of reliability data and low reliability prediction accuracy of remanufactured parts.Therefore,a reliability prediction method for remanufactured parts based on deep transfer learning is proposed. Firstly,the influence of personalized process in the manufacturing stage on the reliability of remanufactured parts is analyzed,the reliability is predicted by combining the operation data of the service stage,and the sample expansion is carried out by introducing the service stage operation data and manufacturing stage process quality data of new products and parts of different models of the same product,and the principal component analysis method is used to obtain key characteristic data such as running time,status information,processing accuracy and other key characteristics that affect reliability,and the source domain dataset is constructed. Secondly,a convolutional neural network is used to construct a deep learning model between key feature data and reliability in the source domain,taking the remanufactured parts data as the target domain dataset,and applying the model migration to the reliability prediction of remanufactured parts through the adaptive gradient algorithm to improve the prediction accuracy. Finally,taking the parts that need to be remanufactured in a CNC machine tool as an example,the effectiveness of the proposed prediction method is verified.

Key words: remanufactured parts, reliability prediction, deep learning, transfer learning