Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights
arXiv:2507.09766v2 Announce Type: replace Abstract: Accurate estimation of Remaining Useful Life (RUL) and State of Health (SoH) is essential for reliable Prognostics and Health Management (PHM), supporting timely maintenance and dependable industrial operation. However, hybrid models that combine data-driven learning with physics-based regularization often rely on fixed loss weights and therefore lose accuracy when transferred across assets with different degradation behaviors. This study introduces Reinforced Graph-based Physics-informed Networks with Dynamic Weighting (RGPD), a unified fram
The increasing complexity and mission-critical nature of advanced machinery across industries necessitate more robust and adaptive predictive maintenance solutions, driving innovation in AI for prognostics.
This development improves the reliability and efficiency of capital-intensive assets by enabling more accurate estimations of remaining useful life and state of health, significantly reducing downtime and maintenance costs.
The introduction of dynamic weighting in physics-informed neural networks allows for more accurate and transferable RUL and SoH estimations, moving beyond the limitations of fixed loss weights that hinder broader applicability.
- · Industrial machinery operators
- · Asset management software providers
- · Logistics and supply chain
- · AI/ML R&D companies
- · Traditional preventative maintenance services
- · Manufacturers with unreliable IoT data streams
Widespread adoption of RGPD in critical infrastructure and manufacturing sectors.
Reduced operational expenditures and increased uptime across diverse industrial applications.
New insurance models based on real-time asset health predictions and reduced risk profiles.
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Read at arXiv cs.LG