Muhammad Atif
PhD
DC12 – Uncertainty Assessment and Reduction in AI-Based Fault Detection for Trustworthy Wind Energy Systems
Host organisation
University of Castilla-La Mancha (UCLM), Spain
Project Description
As part of the IntelliWind MSCA Doctoral Network, this project focuses on enhancing the reliability of AI-based fault detection systems in offshore wind turbines by addressing uncertainty in machine learning models. Through a combination of statistical analysis, signal processing, and deep learning, the aim is to develop robust diagnostic frameworks for predictive maintenance. By collaborating with leading academic and industrial partners across Europe, including SYDIS and DTU, the research contributes to improving decision-making processes and operational safety in future wind energy systems.
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Supervisors
Academic Supervisor: Dr. Estefania Artigao Andicoberry
Co-Supervisor: Dr. Sergio Martín Martínez
Background
I am originally from Pakistan, where I earned a bachelor’s degree in Electronics Engineering from UET Peshawar and a master’s degree in Electrical Engineering from COMSATS University Islamabad. My research has focused on AI-driven condition monitoring of electric machines, using techniques like thermal imaging, vibration analysis, and current signature analysis. I’ve worked on developing real-time fault detection systems and image-based diagnostics, leveraging deep learning, signal processing, and coding in Python and MATLAB. I am passionate about generative AI, transformer-based models, and industrial automation.
Motivation
With hands-on experience in AI-based fault diagnosis and a solid coding foundation, I was eager to apply my skills to a high-impact domain like renewable energy. The IntelliWind project offers the perfect environment to explore uncertainty in AI models while contributing to the dependability of wind energy systems. The opportunity to work with leading researchers and industry collaborators, while applying advanced techniques like machine learning and uncertainty quantification, aligns perfectly with both my professional goals and personal commitment to building smarter, cleaner energy systems.
