Meta-Learning and Knowledge Discovery based Physics-Informed Neural Network for Remaining Useful Life Prediction

Yu Wang, Shujie Liu, Shuai Lv, Gengshuo Liu
4/18/2025
cs.LGcs.AI

No official code found. Best implementation: Sephiroth66616/MKDPINN (25 stars, MIT, last commit 2025-04-21).

Abstract

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

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Code Implementations(1)

Sephiroth66616/MKDPINNCommunity100%
MIT

A novel approach for Remaining Useful Life (RUL) prediction, combining meta-learning, knowledge discovery, and Physics-Informed Neural Networks (PINNs). This repository contains the code for the paper "Meta-Learning and Knowledge Discovery based Physics-Informed Neural Network for Remaining Useful Life Prediction of Rotating Machinery".

252Python, Jupyter NotebookMar 31, 20251 years agoMIT

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Cite this paper

@article{wang2025metalearning,
  title  = {Meta-Learning and Knowledge Discovery based Physics-Informed Neural Network for Remaining Useful Life Prediction},
  author = {Yu Wang and Shujie Liu and Shuai Lv and Gengshuo Liu},
  year   = {2025},
  eprint = {2504.13797},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.13797v1}
}

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