# An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
###### Abstract
[12] put forward a deep convolutional migration network based on spatial pyramid pooling (SPP-CNNTL) that uses transfer learning (TL) with a maximum mean deviation (MMD) measurement function in RUL prediction and solves the difference in data distribution under different failure types. [PERSON] et al. [13] put forward a two-stage TR-CNN method based on transfer learning for the prediction of the RUL of bearings; the model is trained in the form of domain invariance by minimizing the probability distribution distance. This method solves the model prediction problem caused by the distribution difference between two datasets and improves the migration ability of the model. [PERSON] et al. [14] put forward a sparse domain adaptation network (SDAN) to solve the problem of data distribution difference caused by different operation conditions. This method combines domain adversarial learning and sparse domain alignment in sparse domain adaptation, which guides SDAN to learn domain-invariant features and improves the RUL prediction performance of bearings under different working conditions. [PERSON] et al. [15] put forward a new generative adversarial network (GAN) based on the prognostic method for RUL prediction and integrated the training process of bearing health predictors into the GAN architecture. GAN generates synthetic degradation data based on available time series degradation data; then, the model acquires knowledge from both training data and synthetic data, thereby solving the problem of data imbalance and enhancing the prediction performance of the model. [PERSON] et al. [16] put forward a novel Bole convolution (BC) neural network with a tandem three-direction attention (TDA) mechanism (BTA-Net) for the classification of small samples and unbalanced data, which eliminates redundant information and achieves feature mining. Research on data-driven machinery RUL prediction has made considerable headway in recent years. In particular, deep transfer learning methods have been extensively applied to machinery health prediction, which uses knowledge learned from labeled data in the source domain to identify health information represented by unlabeled data in the target domain. Successful application of these methods relies on the assumption that cross-domain label spaces are the same. Smart models focus on the global features of data when extracting deep features but neglect or pay little attention to the useful local target features, which compromises the accuracy and reliability of the models in learning features. To address the problem that existing models overlook useful local target features and cannot extract features in a selective manner, we consider the contribution of input data to the modeling output and propose an attention-based RUL prediction method for rotating machinery. Specifically, the performance evolution of rotating machinery is classified into different states, and an attention mechanism is introduced to assign varied weights to the GRU inputs so that the model can extract more important information for RUL prediction and achieve accurate extraction of both global and local features of the target object, thereby achieving higher accuracy and stability in prediction.