Tool Condition Prognostic Model Based on Digital Twin System

Based on the interconnection of physical and virtual devices, the fault prognostic driven by Digital twin becomes a new methodology that diagnoses fault phenomena quickly. In this paper, a tool condition prognostic model based on digital twin(TCP-DT) is proposed to improve prognostic accuracy and reduce response time. The time series data such as the vibration and current signals are used as input information. Moreover, the Long Short-Term Memory(LSTM)-RNN algorithm is employed to learn the features from signals automatically. Then, the Softmax regression method is applied to classify the tool conditions. Experimental results show the model has good effectiveness and generalization.