Machine Learning for Fault Diagnosis of Wind Turbines, 2nd Edition
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 372
Special Issue Editor
Interests: process monitoring; fault diagnosis and prediction mechanical system signal processing intelligent maintenance system target tracking; action recognition and unknown environment navigation of service robot
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Special Issue Information
Dear Colleagues,
Following the success of the previous Special Issue, titled “Machine Learning for Fault Diagnosis of Wind Turbines” (https://www.mdpi.com/journal/machines/special_issues/faultdiagnosis_machines), we are pleased to announce the next in the series, entitled “Machine Learning for Fault Diagnosis of Wind Turbines, 2nd Edition”.
With the increasing consumption of fossil fuels and problems with the gradual deterioration in the environment, there is an urgent need to find a clean and renewable energy source. Wind energy is irreplaceable in energy structures owing to its rapid growth. Usually, wind power generators are installed in remote areas or offshore areas where traffic is inconvenient. The gearbox is generally installed tens or even hundreds of meters above the ground in the sky, and it is subjected to complex operating conditions, which makes daily monitoring and maintenance of wind turbines difficult. Once a problem occurs, it significantly reduces profits for a wind farm. Therefore, fault diagnosis and maintenance are critically important during the operation of wind turbines.
In recent years, machine learning has played a crucial role as an emerging technology for fault diagnosis in wind power systems. Over recent decades, researchers have proposed different methodologies for dealing with the issues related to the fault diagnosis of wind turbines; however, there are still some challenges encountered in many aspects. Advances in machine learning can provide the tools and foundations for creating fascinating data-driven end-to-end solutions for the fault diagnosis of wind turbines.
This Special Issue invites researchers and industrial professionals to investigate and present recent advances and techniques addressing problems in the fault diagnosis of wind turbine using machine learning.
Dr. Gang Yu
Guest Editor
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Keywords
- machine learning
- fault diagnosis
- wind turbine
- deep learning
- condition monitoring
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Related Special Issue
- Machine Learning for Fault Diagnosis of Wind Turbines in Machines (3 articles)
