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.

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Enhancing Wind Turbine Prognostics: Bridging Data and Physics for Sustainable Energy