# An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
###### Abstract  
Data imbalance and large data probability distribution discrepancies are major factors that reduce the accuracy of remaining useful life (RUL) prediction of high-reliability rotating machinery. In feature extraction, most deep transfer learning models consider the overall features but rarely attend to the local target features that are useful for RUL prediction; insufficient attention paid to local features reduces the accuracy and reliability of prediction. By considering the contribution of input data to the modeling output, a deep learning model that incorporates the attention mechanism in feature selection and extraction is proposed in our work; an unsupervised clustering method for classification of rotating machinery performance state evolution is put forward, and a similarity function is used to calculate the expected attention of input data to build an input data extraction attention module; the module is then fused with a gated recurrent unit (GRU), a variant of a recurrent neural network, to construct an attention-GRU model that combines prediction calculation and weight calculation for RUL prediction. Tests on public datasets show that the attention-GRU model outperforms traditional GRU and LSTM in RUL prediction, achieves less prediction error, and improves the performance and stability of the model. rotating machinery; remaining useful life prediction; data imbalance; gated neural network; attention mechanism +
Footnote †: journal: Medical  
network (GAN) is used to learn noise distribution and map relations between real machine vibration data in the time domain; then, explicit expressions are used to create an expanded dataset of real false samples to solve the problem of data imbalance. [PERSON] et al. [5] proposed a domain adaptation technique with a maximum mean discrepancy metric to learn generalized diagnostic features so that the health identification model learned from the training machines can be effectively applied to new machines. [PERSON] et al. [6] put forward a domain adversarial transfer network (DATN), which employs task-specific feature learning networks and domain adversarial training techniques to address large distribution discrepancies across domains. [PERSON] [7] put forward a deep convolution domain adversarial transfer learning (DCDATL) model for fault diagnosis of rolling bearings; specifically, joint distribution of labeled samples in the auxiliary domain and unlabeled samples in the target domain is creatively used for domain adversarial training, which enhances the adaptability of samples in the auxiliary domain to the target domain and improves the transfer performance of the method. As there are few labeled fault data in real-world production, [PERSON] et al. [8] put forward a deep-learning-based subdomain adaptation method for gear fault diagnosis that extracts transferable features from fault data and exploits multikernel local maximum differences to measure the distribution discrepancy of transferable features in relevant subdomains. [PERSON] et al. [9] put forward an attention-aware face recognition method based on a deep convolutional neural network (CNN) and reinforcement learning, which achieved satisfactory recognition performance on face verification databases. [PERSON] et al. [10] put forth a novel quadratic polynomial-guided fuzzy C-means and dual-attention (QPFC-DA) mechanism composite network model architecture to solve the problem of limited segmentation performance as a result of high complexity and noise in medical images. [PERSON] et al. [11] put forward a joint weak saliency and attention-aware (JWSAA) method for person reidentification. Their method uses a weak significance mechanism to remove background information from the image and focuses on persons in the image; the features of different parts of the person are adaptively extracted through an attention perception module. [PERSON] et al.