Remaining useful life prediction method for cross- condition tools based on parallel fusion

Abstract The current tool remaining useful life (RUL) prediction models do not fully consider the importance of signals from different sensors and the differences in working conditions, which leads to a decline in the model’s robust performance under new conditions. This paper introduces a novel cross-condition RUL prediction approach that integrates parallel fusion, transfer learning, and deep learning. Considering the characteristics of different data sources, correlated signals are initially selected using correlation analysis and dynamic time warping (DTW). Subsequently, multi-scale Depthwise Separable Convolution and multi-attention mechanisms are combined to capture the importance of features at various scales. Finally, a dynamic domain adaptation technique is employed to adjust the importance weights of conditional distribution discrepancy and marginal distribution discrepancy, enabling the model to adapt to cross-condition scenarios.