Multi-source online transfer learning based on hybrid physics-data model for cross-condition tool health monitoring

Abstract

Prognostic maintenance (PM) aims to monitor the running status and promptly detect potential failures to improve the availability and productivity of the equipment. The dimensional accuracy and surface integrity of the machined parts are directly influenced by the cutting tools. Thus, tool health monitoring (THM) is crucial to ensure the optimal in-service performance of the parts. Nevertheless, the variability of operating conditions, including milling parameters, workpiece materials, etc., typically results in insufficient fault data to train the model for new conditions, thus presenting a challenge in predicting the remaining useful life (RUL) of cutting tools. To address the above issue, this study proposes a multi-source online transfer learning framework for predicting the RUL of cutting tools cross various operating conditions. A source selection strategy is initially proposed to filter the source conditions that contribute to the target modeling from the numerous candidate operating conditions. Then, online transfer learning is employed to transfer valuable knowledge from source domains to target domains while updating the target data online to reflect the actual machining scene. In contrast to the traditional transfer learning approaches, this study utilizes a hybrid physics-data model as the base learner to improve the predictive precision of the RUL in the future scenarios. The results demonstrate its generalizability and flexibility in accurately tracking tool degradation status, and the prediction accuracy of the RUL reaches more than 93 % in various target operating conditions. This study provides reliable technical support for THM in machining actual complex components.