Abstract
Modernized equipment features a multi-component structure, which is characterized by complex degradation behaviors, noisy monitoring measurements, and considerable uncertainty in life prediction, making accurate remaining useful life (RUL) forecasting a challenging task. To address this issue, this paper takes an aero-engine as a typical research subject and proposes a multi-component systems RUL prediction method that integrates physical priors and a variational spatiotemporal graph convolutional network. Firstly, graph-structured data are constructed according to the physical topological relationships among multiple engine components, and an initial adjacency matrix is generated to endow the model with prior physical knowledge of sensor dependencies. Secondly, a spatiotemporal feature extraction module is established, which combines graph convolution, dual attention mechanisms, and temporal convolution to adaptively select informative sensor channels and critical spatiotemporal features. Finally, a variational information bottleneck mechanism is introduced. By means of mask-based noise reduction and dynamically learned probabilistic graph structures, information relevant to life-related characteristics is screened, and the processed features are fed into a multi-layer perceptron to implement RUL regression prediction. Validations on the public C-MAPSS aero-engine dataset demonstrate that the proposed method adequately combines the merits of physical priors and data-driven paradigms. It effectively mitigates noise interference and reduces prediction uncertainty, and can provide technical support for health management and life prediction of multi-component aero-engine systems.