To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is difficult to monitor in real-time. Meanwhile, external uncertainties significantly impact bearing degradation. Therefore, this paper proposes a new bearing RUL prediction method based on long-short term memory (LSTM) with uncertainty quantification. First, a fusion metric related to runtime (or degradation) is proposed to reflect the latent degradation process. Then, an improved dropout method based on nonparametric kernel density is developed to improve estimation accuracy of RUL. The PHM2012 dataset is adopted to verify the proposed method, and comparison results illustrate that the proposed prediction model can accurately obtain the point estimation and probability distribution of the bearing RUL.

Remaining useful life (RUL) is a critical metric in prognostics and health management (PHM) because it reflects the future health status and fault progression of products. Most RUL estimation methods are based on degradation data. In practice, due to changing degradation mechanisms during products' whole life cycle, the degradation data may consist of two or more distinct phases, and the time points of these mechanisms switching are usually nondeterministic. This property makes RUL estimation a difficult task. To solve this problem, this paper proposes a switchable state-space degradation model to characterize degradation paths with nondeterministic switching manner dynamically. To update the model parameters by newly available data, a novel statistical procedure based on Rao-Blackwellized filter/smoother and an expectation maximization algorithm is derived. To improve the robustness and efficiency of the RUL prediction, a semianalytic prediction model is developed, which can avoid significant fluctuation in RUL estimation. The developed methodologies can automatically track different degradation phases and adaptively update parameters related to prior distributions. Two real products degradation cases are used to verify our methodologies.

In recent years, the estimation of the remaining useful life (RUL) has become an increasingly important topic. Existing RUL estimation studies mainly focus on linear degradation cases or degradation processes that can be linearized. A few nonlinear degradation models often rely on a training process based on a batch of samples obtained from the same population. Consequently, large bias or uncertainties may often occur under varying stress conditions. To address this problem, this article proposes a prognostic approach based on an adaptive particle filter (PF) to predict the RUL of the dynamic degradation systems using system degradation records. First, a nonlinear degradation model based on the fusion of an exponential item and a power law wear model were derived to capture the wear process under varying stress conditions. Second, the PF method was used to update the model parameters by treating the parameters as hidden state variables. Third, an adaptive strategy was derived based on the expectation-maximization algorithm and particle smoother algorithm to recursively update the hidden variables. Finally, an actual magnetic head wear dataset obtained from an actual manufacturing plant is used to verify the effectiveness of the proposed approach. The results reveal that the proposed approach significantly improves the prediction accuracy compared with the PF-based approach and extends Kalman-filter-based approach.