A novel remaining useful life prediction method based on gated attention mechanism capsule neural network

•The gating mechanism of the proposed GAM can effectively minimize the effect of noise in the sensor data and improve the anti-interference ability of the model. Moreover, the GAM enables the model to pay more attention to important features.•The CapsNet is employed to extract features from the weighted sensor data and improve the prediction performance of the GAM-CapsNet model.•In order to further improve the applicability of the model, the Bayesian layer is implemented in the GAM-CapsNet to quantify the uncertainty of the predicted RUL.•Two representative datasets of turbofan engines and cutter wear demonstrated the effectiveness and superiority of the GAM-CapsNet model. High-accuracy remaining useful life (RUL) prediction is helpful to make in-time maintenance scheduling, reduce the waste of resources, and prevent the occurrence of serious accidents. Currently, data-driven RUL prediction methods are widely used in engineering fields due to their simplicity, efficiency, and robustness. In data-driven methods, the RUL is predicted by learning the mapping from the sensor data to the RUL of machinery. However, the sensor data are often disturbed by noises, and the existence of noise can negatively affect the follow-up RUL prediction. Moreover, the uncertainty of the predicted RUL is often ignored. To address the issues, this paper proposes a novel gated attention mechanism capsule neural network (GAM-CapsNet). A gated attention mechanism (GAM) is developed to increase the anti-interference ability of the model against noises and assign large weights to the most important features. In order to improve the feature extraction ability of the model and quantify the uncertainty of the RUL prediction, the primary capsule, digital capsule, and Bayesian layer are implemented in the proposed GAM-CapsNet. The effectiveness and superiority of the GAM-CapsNet model are verified on turbine engine and cutter wear datasets. Compared to state-of-the-art methods, the prediction ability of the GAM-CapsNet on four engine datasets is improved by 1.11%, 13.44%, 3.06%, and 12.49%, respectively. In addition, the prediction ability of the GAM-CapsNet on three cutter wear datasets is improved by 33.82%, 58.95%, and 36.33%, respectively. The experimental results indicate that the GAM-CapsNet model has better prediction performance.