Advances in Remaining Useful Life (RUL) Prediction and Predictive Maintenance of Industrial Equipment
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 27
Special Issue Editors
Interests: system reliability; dynamical systems; data modeling and forecasting; decision theory; measurement error
Interests: turbomachinery; fluid dynamics; green industries; renewable energy systems
2. R & D Department, Bodor laser Co., Jinan, China
Interests: smart manufacturing; machine learning; AI-driven predictive modeling and optimization; laser material processing; digital twins in manufacturing; intelligent control systems
Special Issue Information
Dear Colleagues,
The evolution toward Industry 4.0 has initiated a paradigm shift in industrial maintenance, moving from traditional reactive or scheduled interventions to data-driven predictive strategies. Central to this transformation is the ability to accurately predict the remaining useful life (RUL) of machinery, which stands as a cornerstone for optimizing operational efficiency, ensuring system reliability, and minimizing economic losses. The proliferation of advanced sensors and the Industrial Internet of Things (IIoT) now provides an unprecedented volume of data, creating fertile ground for the development of sophisticated prognostic models.
This Special Issue seeks to collate seminal research and cutting-edge applications focused on RUL prediction for industrial equipment. We welcome high-quality submissions on novel machine learning and deep learning models, hybrid approaches that synergize physics-based principles with data-driven techniques, advanced methods for sensor data fusion, and compelling real-world case studies that demonstrate the tangible impact of predictive maintenance systems. By showcasing these advancements, this Issue aims to accelerate the deployment of intelligent health management systems across modern industries.
Prof. Dr. Huitian Lu
Dr. Mohammad Omidi
Dr. Ali Naderi Bakhtiyari
Guest Editors
Manuscript Submission Information
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Keywords
- remaining useful life (RUL)
- prognostics and health management (PHM)
- performance degrading and assessment
- predictive maintenance
- deep learning
- fault diagnosis
- data-driven prognostics
- turbomachinery
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