IoT-Driven Predictive Maintenance For Wind Turbines

Olamide Abimbola, Oluwafemi Tayo Ojo, Ebenezer Fagbola, Usman Abdullahi Idris, Muhammad Bolakale Salman

Abstract

Wind turbines are critical components of renewable energy infrastructure, yet their maintenance poses significant challenges due to unpredictable failures and high operational costs. This paper presents an IoT-driven predictive maintenance framework for wind turbines, leveraging advanced sensors, machine learning algorithms, and real-time data analytics. Our approach enables proactive maintenance, reduces downtime, and optimises energy production by continuously monitoring turbine performance, detecting anomalies, and predicting potential failures. We detail the system architecture, implementation, and results, demonstrating the effectiveness of the proposed framework. The study highlights the transformative potential of IoT-driven predictive maintenance in enhancing wind energy systems' reliability and efficiency while outlining future research directions to advance this field further.

10.22178/pos.114-20


Keywords


Predictive Maintenance; Wind Turbines; Internet of Things (IoT); Machine Learning; Real-Time Data Analytics

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References


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Copyright (c) 2025 Olamide Abimbola, Oluwafemi Tayo Ojo, Ebenezer Fagbola, Usman Abdullahi Idris, Muhammad Bolakale Salman

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