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

Lanjun Wan,Changyun Li,Zhibing Wang,Yuanyuan Li,Yuezhong Wu,Keyu Chen

doi:10.1109/access.2021.3140165

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.

Highlights

The cutting tool directly contacts the workpieces in the computerized numerical control (CNC) machining process, which is the most important link to determine the machining accuracy and mechanical performance of the workpieces [1], [2]
A novel remaining useful life prediction method based on CEEMDAN-improved fine-to-coarse reconstruction (IFTC)-phase space reconstruction (PSR) and ensemble convolutional neural network (CNN)/bi-directional long short-term memory (BiLSTM) model for cutting tool is proposed, which can effectively predict the RUL of cutting tool
A series of experiments are conducted to verify the effectiveness of the proposed RUL prediction method using the cutting tool vibration signals collected from the actual CNC machining process, and the results show that the proposed method obtains a high score of RUL prediction for cutting tool

Summary

Introduction

The cutting tool directly contacts the workpieces in the computerized numerical control (CNC) machining process, which is the most important link to determine the machining accuracy and mechanical performance of the workpieces [1], [2]. This is because the IFTC can mine the local continuity and integrity features from IMF components, and the one-dimensional chaotic cutting tool original vibration signals can be mapped into the high-dimensional phase space using PSR, the RUL prediction method with IFTC-PSR achieves better prediction performance.

Results

Conclusion