Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions

Many data-driven remaining useful life (RUL) prediction methods usually assume that the training and test data are independent and identically distributed. However, the different degradation trends of machines under variable working conditions can lead to problems with disparate distribution of degradation features and difficulties in obtaining the corresponding labels. To address the above problems, this paper proposed a RUL prediction method based on a transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions. Firstly, a shrinkage attention module is designed by using the attention mechanism and shrinkage operation to eliminate the interference of irrelevant information and increase the focus on critical features. Secondly, a multi-stage shrinkage attention temporal convolution block based on a hybrid attention subnetwork and soft thresholding subnetwork is designed to efficiently learn the manifold structure of the input data to capture the degenerate information-rich deep features. Finally, an unsupervised domain adaptation strategy based on representation subspace distance and bases mismatch penalization is proposed to enhance the learning of cross-domain invariant features. The proposed method is experimentally studied on XJTU-SY and FEMTO datasets. The experimental results demonstrate that the effectiveness and accuracy of the proposed method in RUL prediction are higher than other methods.