基 于 实 例 迁 移 学 习 的 跨 工 况 刀 具 剩 余 寿 命 预 测

Accurate and reliable predictions of tool remaining useful life could reduce the rate of over-utilization and under-utilization of tools during machining，thereby maximizing the machining reliability and reducing production costs. Traditional machine learning methods for tool remaining useful life prediction rely heavily on the assumption that training and test data follow the same distribution，as well as extensive offline measurement data. However，in actual machining process，prediction accuracy of the traditional methods is reduced due to the variation in machining conditions and limited tool wear data. To address this problem，an Instance-based Transfer Learning framework is proposed to accurately predict the tool remaining useful life cross different working conditions. Firstly，a transfer learning algorithm is used to dynamically adjust the weights of all instances in multiple source domains，which aims to make full use of the source domain information that is highly correlated with the target data. Thus，the generalization ability of the model is improved，and the remaining tool life of the target working conditions could be well predicted with only a small amount of target domain data. Secondly，recurrent Gaussian process regression model is further developed as the base learner to improve the time series prediction capability of the transfer learning algorithm. The model limits the tool remaining useful life at adjacent moments through delayed feedback，while reducing the feature preparation time and the model complexity are reduced. The results indicate that the proposed framework can effectively improve the prediction accuracy of the tool remaining useful life cross different working conditions，and the prediction effectiveness also confirms the stability and reliability of the framework.