Meta-Learning and Knowledge Discovery based Physics-Informed Neural Network for Remaining Useful Life Prediction
This addresses a critical issue for industrial safety and maintenance by enabling few-shot adaptation to new tasks, though it appears incremental as it builds on existing physics-informed neural network and meta-learning approaches.
The paper tackles the problem of predicting remaining useful life (RUL) for rotating machinery under data scarcity by proposing a meta-learning and knowledge discovery-based physics-informed neural network (MKDPINN), which outperforms baselines in generalization and accuracy on industrial and benchmark datasets.
Predicting the remaining useful life (RUL) of rotating machinery is critical for industrial safety and maintenance, but existing methods struggle with scarce target-domain data and unclear degradation dynamics. We propose a Meta-Learning and Knowledge Discovery-based Physics-Informed Neural Network (MKDPINN) to address these challenges. The method first maps noisy sensor data to a low-dimensional hidden state space via a Hidden State Mapper (HSM). A Physics-Guided Regulator (PGR) then learns unknown nonlinear PDEs governing degradation evolution, embedding these physical constraints into the PINN framework. This integrates data-driven and physics-based approaches. The framework uses meta-learning, optimizing across source-domain meta-tasks to enable few-shot adaptation to new target tasks. Experiments on industrial data and the C-MAPSS benchmark show MKDPINN outperforms baselines in generalization and accuracy, proving its effectiveness for RUL prediction under data scarcity