JC
juan carlos jauregui
Autonomous University of Queretaro
Santiago de Querétaro, Mexico
About this Research Topic
This Research Topic is currently accepting articles, but is closing soon.
Background
This research topic addresses the pressing open questions in wind turbine prognostics and health management (PHM). Despite the benefits of PHM, its application is limited by challenges such as uncertainty management, data quality issues, and the integration of physics-based models with data-driven approaches. The study explores the current state of PHM in wind turbines, emphasizing the need for improved RUL prediction models and the fusion of diverse data sources. By tackling these issues, researchers can pave the way for more reliable and efficient wind turbine operation, contributing to sustainable energy solutions. This exploration aims to identify gaps in current methodologies and propose innovative solutions to enhance PHM strategies in the wind energy sector.
Goal:
The goal of this Research Topic is to gather innovative research results and ideas that improves wind turbine prognostics and health management. We aim to highlight new methods for fault detection, remaining useful life prediction, and the integration of data-driven and physics-based approaches, supporting more reliable and efficient wind energy systems.
Scope and Information for Authors:
This Research Topic welcomes contributions including, but not limited to:
• Hybrid approaches that combine data-driven and physics-based models for wind turbine prognostics
• Advanced techniques for fault detection and remaining useful life (RUL) prediction
• Methods to improve data quality and integrate diverse monitoring sources
• Development and application of digital twins or simulation-driven health management
• Real-world case studies and validation of prognostic solutions in wind energy systems
Contributions from both academia and industry are encouraged. Submissions may address theory, practical deployment, or interdisciplinary solutions to support more reliable and efficient wind energy systems.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
- Brief Research Report
- Data Report
- Editorial
- FAIR² Data
- General Commentary
- Hypothesis and Theory
- Methods
- Mini Review
- Opinion
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
- Brief Research Report
- Data Report
- Editorial
- FAIR² Data
- General Commentary
- Hypothesis and Theory
- Methods
- Mini Review
- Opinion
- Original Research
- Perspective
- Policy Brief
- Review
- Technology and Code
Keywords: Machine learning, Data-driven modeling, Control and optimization, Structural dynamics and vibration control, Artificial intelligence, Signal and data analysis, wind energy systems
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
