Picture-in-Picture Strategy-Based Complex Graph Neural Network for Remaining Useful Life Prediction of Rotating Machinery

Graph neural networks (GNNs) are increasingly explored in the field of prognostics and health management (PHM) due to their excellent performance when dealing with non-Euclidean data. However, current GNNs are mostly based on real domain modeling. In addition, existing graph construction methods rely on the prior positional relationship of multiple sensors. In view of the above, this article proposes complex GNNs based on the picture-in-picture strategy (CGNN-PIP) to realize the remaining useful life (RUL) prediction of rotating machinery under multichannel signals. Specifically, the classical graph convolution operation is upgraded to generalized complex graph convolution, and a complex graph neural network (CGNN) is further constructed to extract deep degenerate feature representations. Meanwhile, the picture-in-picture (PIP) strategy is designed to guide graph construction, which takes the single-path graph as a node of the new graph to build a deeper-level graph. We verified the effectiveness and superiority of the proposed method through two case studies on different run-to-failure datasets. The results show that the proposed CGCN-PIP can reasonably construct the topology map of the complex domain data and extract the temporal and structural information reflecting the equipment degradation. The comparison with state-of-the-art methods also proves that CGCN-PIP has advantages in terms of prediction accuracy and training consumption.