Digital twin–driven causal diagnosis mechanism for life health of high-speed spindle system.

In order to achieve the causal diagnosis of life health of high-speed spindle system, a digital twin (DT)–driven prediction method is proposed based on the real-time monitoring of thermal characteristics. The finite element method is used to achieve the DT for thermal characteristics through real time correcting the thermal boundaries using the correction models. The long short-term memory recurrent neural network (LSTM-RNN) is used to predict the heat generations of bearings and motor; the prediction results are used to diagnose the health status according to the domain and threshold models. A heat change rate–based causal diagnosis model is proposed to judge the fault sources. The spindle wear and fault experiments are carried out to verify the effectiveness of the proposed DT system. The experimental results show that the DT accuracy of thermal characteristics exceeds 95%, and the proposed DT system can successfully monitor the health status of the spindle system. [ABSTRACT FROM AUTHOR]