Remaining Useful Life Prediction Modeling Method for Rotating Machinery Based on Tensor Decomposition and Bayesian Neural Networks

Reliable structural health monitoring and risk-informed maintenance of civil infrastructure systems rely on accurate life-cycle assessment of degradation and remaining useful life (RUL) of critical rotating machinery components. In such systems, rolling bearings are among the most failure-sensitive and maintenance-critical units. Rotating machinery, which is widely used in civil engineering systems for functions such as pumping, ventilation, and energy conversion, is particularly susceptible to performance degradation and failure. Owing to material fatigue, local damage evolution, and environmental disturbances, key performance indicators often exhibit nonlinearity, individual variability, and time-varying fluctuations. As a result, the degradation process of rotating machinery becomes highly uncertain, posing critical challenges for reliable RUL prediction. To address this issue, this paper proposes a RUL prediction method that integrates tensor decomposition with a Bayesian neural network (BNN). First, Bayesian tensor decomposition is applied to perform dimensionality reduction and feature extraction on high-dimensional monitoring data, thereby identifying key latent degradation features that form the basis for accurate prediction. On this basis, a probabilistic modeling framework is established, in which Bayesian tensor decomposition characterizes the variability of the extracted features and provides data-driven uncertainty estimates as prior information for the BNN. The extracted features are then fed into the BNN to achieve probabilistic RUL prediction while explicitly accounting for data uncertainty. Experimental results showed that the proposed approach mitigates interference from varying operating conditions, captures degradation trends, and improves prediction accuracy and robustness with reliable uncertainty quantification. These advantages highlight the potential of the proposed framework for supporting condition-based maintenance, risk-informed decision-making, and life-cycle reliability management of civil infrastructure systems in practical applications.