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Bearing is a key component in rotary machines. Their failures may cause the abrupt shutdown of these machines, which would result in substantial economic losses. Therefore, the prediction of the remaining useful life (RUL) of bearings is regarded as one of the critical approaches to avoid failure of bearings and their systems. In this article, an ensemble data-driven approach is proposed to predict the RUL of bearings. It uses feature extraction, an attention mechanism, and uncertainty analysis. First, the features embedded in the bearings’ vibration signals are extracted. Second, a stacked gated recurrent unit (GRU) is constructed to predict the bearing RUL. A novel attention mechanism based on dynamic time warping (DTW) is developed to improve the performance of information extraction, and a Bayesian approach is employed to analyze the prediction uncertainty. Finally, the proposed approach is validated using two benchmark-bearing data sets. The results show that the proposed approach can predict the bearing RUL effectively, and the prediction uncertainty can also be evaluated. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based methods for remaining useful life (RUL) prediction have received increasing research attention due to excellent feature extraction abilities. Most DL methods rely on abundant labeled samples for supervised training. However, because of the adoption of the over-maintenance strategy of equipment, the monitored data for the degradation of equipment usually consists of few labeled samples and a large amount of unlabeled samples, which limits the performance of DL methods. To take advantage of the value of unlabeled samples, this paper proposed a contrastive learning framework for RUL prediction. First, an unlabeled sample augmentation is developed firstly to extend the sample set. Then, an unlabeled sample learning (USL) architecture is proposed to learn the information of degradation from unlabeled samples to promote general DL models' performance on RUL prediction. Based on the proposed framework, USL-convolutional neural network and USL-long short-term memory network are used to validate its performance based on datasets of turbofan engine and bearing. Results show that the performance of RUL prediction based on the proposed framework can be enhanced by unlabeled samples and verify the good scalability and generalization ability of the proposed framework. • Proposed framework boosts RUL prediction with unlabeled samples despite few labeled. • Proposed framework is designed for general deep learning methods like CNN and RNN. • Two case analyses validated the effectiveness and generalization ability. [ABSTRACT FROM AUTHOR]

The issue of uncertainty in Remaining Useful Life (RUL) prediction based on the Deep Learning (DL) framework has gained increased attention in recent years. The probabilistic distribution is usually the effective way to capture the associated uncertainty in RUL prediction. However, most existing research relies on a single distribution that assumes a singular underlying pattern in the data. This type of approach restricts the ability to describe dynamic industrial processes and lacks robustness in adapting to the complexity of time-varying degradation. This paper proposes an approach to improve uncertainty quantification in RUL prediction based on the Multi-Distribution Fusion (MDF) structure. Initially, multiple possible RUL prediction results are produced. Subsequently, the MDF is used to integrate the former results with different weights and outputs the final RUL prediction distribution. The proposed method excels in uncertainty capturing in complex scenarios and provides a deeper understanding of the underlying dynamics of the monitoring data. The application of MDF resulted in more enhanced and robust uncertainty concerning RUL predictions. To validate the effectiveness of the proposed method, two neural networks, the Long Short-Term Memory (LSTM) network and Convolutional Neural Network (CNN), are individually combined with MDF and applied to the C-MAPSS datasets. • The concept of multi-distribution is introduced to quantify the aleatoric uncertainty. • The proposed structure enhances the performance of RUL prediction with uncertainty quantification. • The proposed framework is designed for general deep learning methods, such as CNN and RNN. [ABSTRACT FROM AUTHOR]

Data-driven methods routinely achieve promising results on remaining useful life prediction, but under a window-manner end-to-end paradigm, they suffer from unsatisfying generalization ability and low interpretability, as the consequence of neglecting diverse modes among the entire degradation processes of different entities. This article proposes a novel Transformer-based network, to tackle the problem by integration of global and local information. During offline training, the paired inputs containing full life and piece data are constructed, and then using cross-attention between the encoder and the decoder, the consistent position of the piece data in the full life is derived, which is directly associated with the degradation state. The designed paired inputs and model architecture ensures the strong generalization because the prediction result considering global information is adaptive to diverse degradation modes. Further, the designed cross-attention discrepancy utilizes prior knowledge of the consistent position such that similar degradation states are aligned more properly. Such a consistent position, visualized by the cross-attention distribution, is supposed to represent the intuitive relationship between degradation level and monitoring data, thus provides inherent interpretability about the prediction process. Finally, predictions of the online monitoring piece data with respect to all historical full lives with different degradation modes are aggregated to the final prediction. Extensive experiments on two datasets of turbofan and bearing show that our model provides competitive performance, especially under complicated working conditions and fault modes, achieving averagely 5.9% score reduction compared with the state-of-the-art method. • The union consideration of global and local information ensures adaptation to diverse entities. • The paired inputs of full life and piece data derive interpretable consistent positions. • Two case studies on typical industrial applications validate the effectiveness. [ABSTRACT FROM AUTHOR]