Data-Driven RUL Prediction: Innovations in Generalization, Uncertainty, and Efficiency for Industrial PHM
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 177
Special Issue Editors
Interests: diagnosis and prognosis; embedded-interpretable AI; signal processing
Interests: gas turbine aerodynamics and heat transfer; computational fluid dynamics; AI-driven system modeling, optimization and control; prognostics and health management
Interests: weak signal detection; mechanical fault diagnosis.
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Prognostics and health management (PHM) is crucial for industrial safety and sustainability. This Special Issue seeks cutting-edge data-driven approaches for remaining useful life (RUL) prediction, addressing critical challenges including cross-domain generalization, long-term degradation extrapolation, uncertainty quantification, noise-robust multi-sensor fusion, and edge-deployable lightweight design. We invite original research leveraging emerging methodologies, such as generative trajectory modeling, physics-informed neural networks (PINNs), LLM-based transfer learning, self-data-driven paradigms, and digital twin-enabled degradation simulation. Submissions must demonstrate significant advances in prediction accuracy, robustness, or interpretability, validated in industrial applications (manufacturing, aerospace, energy, transportation, etc.). This Special Issue aims to bridge theoretical innovation with engineering reliability, fostering next-generation predictive maintenance frameworks.
Dr. Diwang Ruan
Prof. Dr. Jianping Yan
Dr. Mengdi Li
Prof. Dr. Junxing Li
Guest Editors
Manuscript Submission Information
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Keywords
- prognostics and health management (PHM)
- remaining useful life (RUL) prediction
- data-driven approaches
- cross-domain generalization
- long-term prediction
- uncertainty quantification
- physics-informed neural networks (PINNs)
- LLM-based transfer learning
- industrial applications
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