Remaining useful life prediction of rotating machine via long short-term memory network with uncertainty quantification

Remaining useful life (RUL) prediction of rotating machinery is critical for intelligent maintenance and ensuring equipment reliability. However, existing methods often struggle to capture the long-term degradation trends and fail to adequately quantify the uncertainty in the predictions. To address these challenges, this paper proposes a novel RUL prediction method based on a long short-term memory-Wiener process-Bayesian optimization (LSTM-WP-Bo) degradation model. First, based on the Wiener process (WP), a long short-term memory (LSTM) network is used to model the drift function of the degradation process. Secondly, based on the concept of first hitting time (FHT), an approximate expression for the probability density function (PDF) of RUL is derived, while the uncertainty of the prediction is quantified. Lastly, the drift and diffusion coefficients are estimated using maximum likelihood estimation (MLE), and the LSTM network's hyperparameters are optimized through Bayesian optimization (Bo). The proposed method is analyzed comparatively on three datasets. For example, after validation on the servo turret power head system degradation dataset, the proposed method achieves a root mean square error (RMSE) of 15.55 and a mean absolute percentage error (MAPE) of 12.91 %, demonstrating significant improvements in prediction accuracy and robustness when compared to existing methods. • Proposed LSTM-WP-Bo model improves RUL prediction and uncertainty quantification. • LSTM serves as the drift function to derive the RUL probability density approximation. • Drift and diffusion coefficients are estimated using the maximum likelihood method. • Bayesian optimization refines LSTM hyperparameters for better performance.