A Novel Remaining Useful Life Prediction Method Based on CEEMDAN-IFTC-PSR and Ensemble CNN/BiLSTM Model for Cutting Tool

Abstract

To accurately predict the remaining useful life (RUL) of cutting tool, a novel RUL prediction method is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose original cutting tool vibration signals to get six intrinsic mode function (IMF) components from each sample. Secondly, high-frequency IMF components and low-frequency IMF components are obtained from IMF components and they are respectively fused into high-frequency data and low-frequency data using the improved fine-to-coarse reconstruction (IFTC), and high-frequency data and low-frequency data are reconstructed using phase space reconstruction (PSR). Thirdly, multiple prediction branches are adopted to construct an ensemble RUL prediction model for cutting tool, the high-frequency data and low-frequency data are input into bi-directional long short-term memory (BiLSTM) and convolutional neural network (CNN) to train a RUL prediction model respectively in each prediction branch. Finally, a series of experiments are conducted to verify the effectiveness of the proposed RUL prediction method, and the results show that the proposed method obtains a high score of RUL prediction for cutting tool.