Tool Remaining Useful Life Prediction Method Based on Time-frequency Features Fusion and Long Short-term Memory Network

As the “teeth” of CNC machine tools, the tool health status directly affects the machining efficiency and the quality of products. However, due to the complex and variable process of tool wear, the tool remaining useful life is difficult to predict. To address the above challenges, we investigate a deep learning-based tool remaining useful life prediction method in this work. First, the time-frequency features of the tool monitoring signal are extracted and fused using time-frequency analysis and wavelet transform methods. And then, a long short-term memory network based tool remaining useful life prediction method is proposed to accurately predict the remaining tool useful life, which can guide the update and optimization of equipment maintenance plans, thus effectively improving the productivity of enterprises, reducing production costs and ensuring product quality. To evaluate the performance of the proposed method, experiments are conducted based on the open dataset of tool health prediction for high-speed CNC machine tools. The experimental results show that the proposed tool remaining useful prediction method based on time-frequency features fusion and long and short-term memory network performs the best prediction performance in terms of mean absolute error, mean squared error, R-squared and other performance indicators compared with the existing classical methods.