Digital twin-driven life health monitoring for motorized spindle

As one of the core components of modern high-speed CNC machine tools, the health state of motorized spindle is closely related to the reliability and stability of CNC machine tools. In order to monitor the health state of motorized spindle real timely, a digital twin-driven life health monitoring system for motorized spindle is designed and developed based on the combined programming of MATLAB, ANSYS, and LabVIEW. The digital twin of thermal characteristics is realized through correcting and mapping the thermal boundaries using the correction models. The real time monitor of health state of motorized spindle is achieved according to the twin temperatures and temperature threshold models. The temperature threshold models are constructed through rotating the temperature domain models according to the allowable temperature rises of motor and bearings. The temperature domain models of front and rear bearings and motor are established based on the Exponential fitting and Lagrange interpolation methods under the conditions of motor idling at various spindle speeds. Experimental results show that the proposed digital twin-driven life health monitoring method can successfully monitor the thermal characteristics and health state of motorized spindle.