Automated Wind Turbines Gearbox Condition Monitoring - A Comparative Study of Machine Learning Techniques Based on Vibration Analysis

Aug 3, 2026

Uploaded by Oliver

Wind turbine gearboxes are prone to failure, and faults often originate from bearing defects or gear wear, causing costly repairs, extended downtime, and major production losses. This study applies machine learning to vibration-based gearbox diagnosis by using fault scenarios and extracting time-domain features from a 750 kW turbine testbed. SVM, Naive Bayes, and KNN classify gearbox faults, with Naive Bayes reaching 95.7% accuracy and effectively linking symptom characteristics to fault patterns. Data-driven intelligent monitoring supports proactive maintenance and improved turbine reliability for renewable energy development.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Challenges in wind turbine reliability and gearbox faults

Condition monitoring and predictive maintenance value

Ahmed Ali Farhan Ogaili Department of Mechanical Engineering University of Mustanisiryah, Baghdad Iraq Kamal Abdulkareem Mohammed Department of Mechanical Engineering University of Mustanisiryah, Baghdad Iraq Alaa Abdulhady Jaber Department of Mechanical Engineering University of Mustanisiryah, Baghdad Iraq Ehsan Sabah Al-Ameen Department of Mechanical Engineering University of Mustanisiryah, Baghdad Iraq Automated Wind Turbines Gearbox Condition Monitoring: A Comparative Study of Machine Learning Techniques Based on Vibration Analysis Wind turbines play a role in the adoption of renewable energy production, but they are susceptible to shutdowns that require thorough monitoring. Gearbox failures are an issue leading to maintenance and operational downtime. This study investigates the application of machine learning methods to enhance the diagnosis of gearbox problems using vibration analysis. Through the application of fault scenarios that impact bearingsand gears, the researchers successfully extracted time domain features from vibration data of a 750 kW turbine testbed in order to detect indications of damage. Support Vector Machine (SVM), Naive Bayes, and K Nearest Neighbour (KNN) machine learning models were used to classify gearbox faults. Among these models, Naive Bayes achieved an accuracy rate of 95. 7%, which exceeded the established benchmarks. The probabilistic approach was able to successfully associate symptom characteristics with fault patterns. Intelligent monitoring systems could improve maintenance efficiency. This data-driven approach highlights the potential of machine learning in supporting wind power development by eliminating gearbox inefficiencies and improving turbine reliability, and further research is being conducted to ensure that this approach works in concert with diversity and in the real world. This shows how machine learning is contributing to advances in renewable energy by helping to analyze predictive problems and prevent costly gearbox failures. Keywords: Gearbox, Data Driven, SVM, KNN, Vibration signal 1. INTRODUCTION Wind turbines play a crucial role in the transition to renewable energy sources, aiding the global push for environmental sustainability [1,2] . Yet, their sustainable operations hinge greatly on their reliability and effectiveness. Among the hurdles that wind turbine operators face is identifying issues in the gearbox, as these can lead to repairs, downtime, and potentially disastrous breakdowns [3,4] . Wind turbines alleviate the environmental consequences of energy generation by transforming the kinetic energy of wind into electricity that is free from emissions [5,6] . Their rapid and significant growth has been propelled by advancements that have improved efficiency and economi-cs, establishing wind power as a crucial contributor to renewable energy [7-9] . Nevertheless, turbines are still vulnerable to numerous mechanical and electrical de– fects that pose a threat to their performance and relia– bility [10] . Environmental factors can cause damage to rotor blades, which can then reduce their aerodynamic efficiency [11,12] . Generators and bearings are suscep– tible to both electrical and mechanical deterioration [13] . Received: March 2024, Accepted: June 2024 Correspondence to: Dr Ahmed Ali Farhan Ogaili Department of Mechanical Engineering, College of Engineering, Mustansiriyah University, Baghdad 100, Iraq, E-mail: [ahmed_ogaili@uomustansiriyah.edu.iq](ahmed_ogaili@uomustansiriyah.edu.iq) doi: 10.5937/fme2403471O © Faculty of Mechanical Engineering, Belgrade. All rights reserved The gearbox is a very fragile component that is prone to failure, which can lead to costly repairs, pro– longed downtime, and even more damage [14] . Gearbox faults account for nearly 25% of wind turbine failures, with downtime costs estimated at $200,000 to $300,000 per incident [15] . These failures, often origi–nating from bearing defects or gear wear,

FAQs

Why is gearbox condition monitoring important for wind turbines?

Gearbox failures are a major cause of turbine downtime and expensive repairs. Monitoring enables early detection of damage so maintenance can be scheduled before severe breakdowns.

How does the study diagnose gearbox problems?

The study uses vibration analysis, applying fault scenarios to extract time-domain features from vibration data, then using machine learning classifiers to identify gearbox faults.

Which machine learning model performed best and what accuracy was reported?

Naive Bayes achieved the highest accuracy at 95.7%, exceeding the established benchmarks and producing strong symptom-to-fault pattern associations.

Aug 3, 2026

Uploaded by Oliver