Adaptive Dynamic Causal Meta Graph-Task Network for Remaining Useful Life Prediction With Extreme Long-Tailed Distribution Condition

In industrial applications, the degradation rate of equipment is often accompanied by stochasticity due to constant changes in operating conditions and loads, making the degradation process tend to have a long-tailed distribution. When equipment fails rapidly due to untimely detection or excessive operation, it tends to produce extreme failure points and extend the tail, leading to the extreme long-tailed distribution. This situation makes it difficult for traditional RUL prediction methods to achieve satisfactory prediction and generalization performance. To address this challenge, this article proposes a new adaptive dynamic causal meta graph-task network for remaining useful life prediction with extreme long-tailed distribution condition. The model uses a data-driven approach in a dynamic graph framework to learn feature information with graph structure and dynamically update the pairwise dependencies of the learned latent features based on task changes. Then, we use causal analysis technique for inferring the distribution of latent representations of causal and outcome variables in the graph structure, which helps to infer the feature structure between latent variables. In addition, multiple balance factors independent of each other are introduced into the dynamic graph structure to adaptively capture meta-task and task-specific shared information, enhancing the model’s ability to capture the global task. Finally, in order to evaluate the proposed model, extensive experiments are conducted on engine and bearing degradation data, and the results validate the superiority of the model.