Dynamic Data-Driven degradation method for monitoring remaining useful life of cutting tools

Monitoring tool condition and remaining useful life (RUL) are vital in preventing the occurrence of excessive tool wear. This paper develops a novel dynamic data-driven degradation method for monitoring the RUL of cutting tools. In sensor-data collection, vibration, sound, and power external sensors and built-in data are gathered from the machine tool. In multi-feature selection and dynamic model updating, a decision-level fusion method of spanning multi-domain features is designed to dynamically utilize a global prediction error for selecting and fusion degradation features, which are associated with the cutting tool life in the degradation model. A rolling HI-RUL mapping is established in RUL prediction by employing a historical health indicator curve, which estimates the RUL of cutting tool with a given threshold. The effectiveness of proposed method was assessed through two run-to-failure experiments of cutting tools, showing that an average global prediction error is reduced to 4.07%.