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Predicting the remaining useful life for operational devices plays a critical role in prognostics and health management. As the models based on the stochastic processes are widely used for characterizing the degradation trajectory, an adaptive skew-Wiener model, which is much more flexible than traditional stochastic process models, is proposed to model the degradation drift of industrial devices. To make full use of the prior knowledge and the historical information, an on-line filtering algorithm is proposed for state estimation, a two-stage algorithm is adopted to estimate unknown parameters as well. For remaining useful life prediction, a novel result is presented with an explicit form based on the closed skew normal distribution. Finally, sufficient Monte Carlo simulations and an application for ball bearings in rotating electrical machines are used to validate our approach. [ABSTRACT FROM AUTHOR]

Nonlinear degradation trajectories are encountered frequently, and not all of them evolve homogeneously in practical systems. To take nonlinearity, heterogeneity, and the entire historical degradation data into account, we propose a nonlinear heterogeneous Wiener process model with an adaptive drift to characterize degradation trajectories. A state-space based method is employed to delineate our model. Due to the introduction of the adaptive drift, it is difficult to directly apply Kalman filter methods to update the distribution of the estimated degradation drift. To address this issue, we develop an online filtering algorithm based on Bayes’ theorem. The expectation-maximization (EM) algorithm, as well as a novel Bayes’-theorem-based smoother, are adopted to estimate the unknown parameters in our model. Moreover, the distribution of the predicted remaining useful life (RUL) incorporating the complete distribution of the estimated degradation drift is achieved analytically. Finally, a simulation, and a case study are provided to validate the proposed approach. [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]