Knowledge and data tandem-driven remaining using life prediction algorithms for few-shot

Abstract How to achieve accurate predictions in a few-shot scenario with limited data is currently a pressing issue. It requires a thorough excavation of domain differences to extract embedded information, improve model generalization, and clarify the decision-making processes, thereby enhancing model credibility and feasibility. To achieve this goal, this study combines the advantages of knowledge-driven and data-driven model, and proposes a knowledge and data tandem-driven algorithm based on deep feature decoupling network (DFDN) to solve the few-shot prediction problem. This method offers both a mechanistic explanation of the employed model and the ability to address few-shot prediction problems. First, the cross-domain metrics of the few-shot data are obtained by an auto-encoding DFDN, and a multi-stage degradation model is constructed. Particle filtering (PF) algorithms and sampling importance resampling techniques are then used to identify the model parameters. The remaining useful life of the bearing is derived through the forward inference of PF and compared to the results of the conventional model for validation. Experiments indicate the superiority and effectiveness of the algorithm approach in few-shot prediction.