Remaining Useful Life Prediction Based on Wear Monitoring with Multi-Attribute GAN Augmentation

With the growing imperative for advanced prognostics and health management (PHM) systems, remaining useful life (RUL) prediction through lubricating oil monitoring has become pivotal for intelligent preventive maintenance. However, existing methodologies face dual challenges: the inherent sparsity of wear monitoring data and the complex interdependencies among multiple indicators, leading to compromised prediction accuracy that fails to satisfy reliability requirements. To address these limitations, this study proposes a novel multi-indicator RUL prediction framework with three technical innovations. First, a fuzzy probabilistic characterization method is proposed to quantify multivariate wear state in the lubricating system, using the weighted fusion of multi-source indicators. Second, a novel CMC-GAN (Centralized Multi-channel Constrained Generative Adversarial Network) architecture is designed. It can increase data using physical knowledge. This solves the problem of sparse data and keeps the important relationships between indicators. Furthermore, we establish a Wiener-process-based degradation model with time-varying coefficients to capture stochastic wear deterioration patterns. The expectation-maximization algorithm with Bayesian updating is employed for real-time parameter calibration, enabling a dynamic derivation of the probability density functions for RUL estimation. Finally, the validity and practicality of the proposed model are verified through actual engineering case studies.