Condition monitoring and life prediction of the turning tool based on extreme learning machine and transfer learning

When the turning tool has worn and failed but the failure is not found, if it continues to be used for processing, it will break, and cause the workpiece to be scrapped, and even damage the machine tool. In order to avoid the loss caused by turning tool wear, the remaining useful life (RUL) prediction of turning tool wear has become a hot research topic in recent years. For RUL prediction in turning tools, the traditional machine is difficult to acquire sufficient degradation data and inconsistent data distribution among different turning tools in engineering, and they cannot provide better prediction accuracy to some extent. To solve the above problems, this paper proposes a multi-granularity feature extraction (MGFE) method based on the gray-level co-occurrence matrix (GLCM) and random forest (RF). Moreover, a health indicator (HI) of turning tools in the source domain was obtained. The common representative features in HI sequence of target domain was transferred to source domain and builds the condition monitoring and life prediction system of turning tools based on extreme learning machine and transfer learning. Finally, extreme vector machine (ELM) is used to construct the RUL prediction model. The research results show that the model constructed in this paper is effective in RUL prediction and can significantly improve the prediction accuracy of remaining useful life.