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Sevinc, Busra (2024) Enhancing Wind Turbine Longevity: A Comparative Study of Deep Learning and Traditional Machine Learning Techniques for Predicting Remaining Useful Life. Masters thesis, Dublin, National College of Ireland.
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Abstract
The aim of this study is to evaluate how effective deep learning is compared to traditional machine learning in predicting the Remaining Useful Life (RUL) wind turbine components. Various models for predicting wear of damage-sensitive gearbox bearings will be evaluated based on their computational requirements and prediction accuracy, and other relevant factors will also be considered in this research. While evaluating the model performances, the lowest value of Mean Absolute Error (MAE) achieved by Random Forest algorithm because it provides good performance and requires less computational cost. This research increases our knowledge of how to predict when machines will need repairs, and also shows how we can apply these ideas in real world predictive maintenance systems for wind turbines.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Jameel Syed, Muslim UNSPECIFIED |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Energy industries Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Artificial Intelligence |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 18 Jun 2025 14:35 |
| Last Modified: | 18 Jun 2025 14:35 |
| URI: | https://norma.ncirl.ie/id/eprint/7925 |
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