JM
J.H. Mendapara
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Increasing awareness about climate change and increasing interest in renewable energy is fueling the rise of wind energy. One of the main challenges currently faced by the wind energy industry is to improve the reliability and availability of wind turbine in order to keep the industry financially attractive. Optimising operations and maintenance (O\&M) strategy through the adoption of cost-effective and reliable detection and prognosis techniques is a clear target for competitive offshore wind development. If the health of components can be accurately monitored then faults can be detected in the early stages and an accurate maintenance plan can be made. This thesis will contribute to this domain of wind energy with original work.
This thesis aims to devise multiple AI techniques capable of performing wind turbine fault detection. These techniques are validated and assessed using experimental data from the wind turbine drive train condition monitoring test rig developed at Durham University which focuses on the most critical wind turbine component, i.e. gearbox. Seeded-fault conditions on the gearbox have been induced/removed from the test rig drive train as required, enabling gearbox tooth damage fault to be implemented repeatedly on demand and under controlled stationary and variable driving conditions.
The raw data from test rig is subjected to data pre-processing tasks such as data cleaning, feature selection and feature extraction. One baseline model (Decision tree), two tree-based models (Random forest and eXtreme Gradient Boosting) and one neural networks model (Multi-layer perceptron) are developed, tuned and compared to achieve the aim of this thesis. The performance of relatively unexplored (in CMS literature) tree-based models is compared to the performance of widely used neural networks model.
Developed models are evaluated based on various performance criteria such as prediction of healthy stage, early stage fault detection, most severe stage fault detection, overall accuracy, overall precision, fault detection rate, false alarm rate, training time, testing time, complexity and fault detection (binary classifiers) performance. Three main models (Random forest, eXtreme Gradient Boosting and Multi-layer perceptron) outperformed the baseline model (Decision tree) in all important evaluation criteria. It is found that eXtreme Gradient Boosting (i.e, a tree-based model) produces the best overall results when compared to other developed models. Hence, it is recommended for the CMS industry to make use of the eXtreme Gradient Boosting machine learning model for the task of wind turbine gearbox fault detection.
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Increasing awareness about climate change and increasing interest in renewable energy is fueling the rise of wind energy. One of the main challenges currently faced by the wind energy industry is to improve the reliability and availability of wind turbine in order to keep the industry financially attractive. Optimising operations and maintenance (O\&M) strategy through the adoption of cost-effective and reliable detection and prognosis techniques is a clear target for competitive offshore wind development. If the health of components can be accurately monitored then faults can be detected in the early stages and an accurate maintenance plan can be made. This thesis will contribute to this domain of wind energy with original work.
This thesis aims to devise multiple AI techniques capable of performing wind turbine fault detection. These techniques are validated and assessed using experimental data from the wind turbine drive train condition monitoring test rig developed at Durham University which focuses on the most critical wind turbine component, i.e. gearbox. Seeded-fault conditions on the gearbox have been induced/removed from the test rig drive train as required, enabling gearbox tooth damage fault to be implemented repeatedly on demand and under controlled stationary and variable driving conditions.
The raw data from test rig is subjected to data pre-processing tasks such as data cleaning, feature selection and feature extraction. One baseline model (Decision tree), two tree-based models (Random forest and eXtreme Gradient Boosting) and one neural networks model (Multi-layer perceptron) are developed, tuned and compared to achieve the aim of this thesis. The performance of relatively unexplored (in CMS literature) tree-based models is compared to the performance of widely used neural networks model.
Developed models are evaluated based on various performance criteria such as prediction of healthy stage, early stage fault detection, most severe stage fault detection, overall accuracy, overall precision, fault detection rate, false alarm rate, training time, testing time, complexity and fault detection (binary classifiers) performance. Three main models (Random forest, eXtreme Gradient Boosting and Multi-layer perceptron) outperformed the baseline model (Decision tree) in all important evaluation criteria. It is found that eXtreme Gradient Boosting (i.e, a tree-based model) produces the best overall results when compared to other developed models. Hence, it is recommended for the CMS industry to make use of the eXtreme Gradient Boosting machine learning model for the task of wind turbine gearbox fault detection.