Tool Remaining Useful Life Prediction Method Based on Multi-Sensor Fusion under Variable Working Conditions

Abstract

Under variable working conditions, the tool status signal is affected by changing machine processing parameters, resulting in a decreased prediction accuracy of the remaining useful life (RUL). Aiming at this problem, a method based on multi-sensor fusion for tool RUL prediction was proposed. Firstly, the factorization machine (FM) was used to extract the nonlinear processing features in the low-frequency condition signal, and the one-dimensional separable convolution was applied to extract tool life state features from multi-channel high-frequency sensor signals. Secondly, the residual attention mechanism was introduced to weight the low-frequency condition characteristics and high-frequency state characteristics, respectively. Finally, the features extracted in the low-frequency and high-frequency parts were input into the full connection layer to integrate working condition information and state information to suppress the influence of variable conditions and improve prediction accuracy. The experimental results demonstrated that the method could predict the remaining life of the tool effectively, and the accuracy and stability of the model are better than several other methods.