Research on prediction method on RUL of motor of CNC machine based on deep learning

To solve the problem of high fault frequency and sudden occurrence of the motor of computer numerical control (CNC) machine tool, the paper proposes a deep learning remaining useful life (RUL) prediction model based on DFS-LSTM. Through collecting the motor life cycle data by sensors, constructing the dataset, then extracting the depth feature set from the original data by DFS（feature depth synthesis), and the depth feature will be inputting into the LSTM(long-short term memory) model for training, then the prediction model is obtained. In order to realise the function of predicting RUL, Deadline time function is designed in data processing, and residual life is calculated by data before Deadline time. The model is applied to the RUL prediction of the motor of computer numerical control (CNC) machine tool, and obtained a good prediction result. © 2021 Inderscience Enterprises Ltd.