Aeroengine Bearing Time-Varying Skidding Assessment With Prior Knowledge-Embedded Dual Feedback Spatial-Temporal GCN

Bearing skidding is the primary factor restricting the development of aeroengines toward ultrahigh speed, low friction, and lightweight. Compared to typical bearing faults, analysis of bearing skidding presents greater challenges due to the weak signal properties, significant time-varying characteristics and coupling influence of multiple factors. It is crucial to fully utilize multisource signals to enhance skidding features and capture time-varying characteristics. This article proposes a prior knowledge-embedded dual feedback spatial-temporal graph convolutional network (DFSTGCN) for skidding assessment. Unlike existing adjacency matrix construction strategies, the correlation between multisource signals is described based on multiple prior knowledge, which includes dynamic model, structural dynamics, and expert experience. Furthermore, a DFSTGCN is designed to simultaneously focus on the spatial and temporal dependencies of time-varying skidding data. Specifically, a dual feedback mechanism that includes prediction error ratio and uncertainty loss function is employed to improve the generalization performance of skidding prediction model. The effectiveness of the proposed strategy is validated under different working conditions.