A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines

Predictive maintenance is essential for ensuring the reliability and efficiency of wind energy systems. Traditional deep learning models for sensor fault detection rely solely on data-driven patterns, often lacking interpretability and robustness. This article proposes a Physics-Guided Bayesian Neur...

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Main Authors: MD Azam Khan, Arifur Rahman, Farhad Uddin Mahmud, Kanchon Kumar Bishnu, Hadiur Rahman Nabil, M. F. Mridha, Md. Jakir Hossen
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of the Computer Society
Subjects:
Online Access:https://ieeexplore.ieee.org/document/11027711/
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author MD Azam Khan
Arifur Rahman
Farhad Uddin Mahmud
Kanchon Kumar Bishnu
Hadiur Rahman Nabil
M. F. Mridha
Md. Jakir Hossen
author_facet MD Azam Khan
Arifur Rahman
Farhad Uddin Mahmud
Kanchon Kumar Bishnu
Hadiur Rahman Nabil
M. F. Mridha
Md. Jakir Hossen
author_sort MD Azam Khan
collection DOAJ
description Predictive maintenance is essential for ensuring the reliability and efficiency of wind energy systems. Traditional deep learning models for sensor fault detection rely solely on data-driven patterns, often lacking interpretability and robustness. This article proposes a Physics-Guided Bayesian Neural Network (PINN-BNN) model that integrates physics-informed learning with Bayesian inference to improve fault detection in wind turbines. The proposed approach enforces domain-specific constraints to ensure physically consistent predictions while quantifying uncertainty for risk-aware decision-making. The model is evaluated using a real-world wind turbine sensor dataset, achieving an accuracy of 97.6%, a recall of 91.8%, and an AUC-ROC of 0.987. The SHapley Additive exPlanations (SHAP) analysis reveals that gearbox temperature, blade vibration, and generator torque are the most critical features influencing failure predictions. Bayesian uncertainty estimation further improves interpretability by assigning confidence levels to each prediction. A comparative study with ten baseline models, including Long Short-Term Memory (LSTM), Transformer-based models, and traditional machine learning classifiers, demonstrates that the PINN-BNN model outperforms existing approaches while maintaining computational efficiency with a training time of 39.8 minutes and an inference time of 1.7 ms per sample. The integration of physics-informed learning ensures that the model generalizes well to varying environmental conditions, reducing false negatives and minimizing unexpected system failures. The proposed methodology presents a step toward interpretable and reliable predictive maintenance in wind energy systems.
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spelling doaj-art-c4d7baf41b724f7ab96b69c68ab5c33e2025-07-03T23:01:02ZengIEEEIEEE Open Journal of the Computer Society2644-12682025-01-01693194210.1109/OJCS.2025.357758811027711A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind TurbinesMD Azam Khan0https://orcid.org/0009-0005-4766-9398Arifur Rahman1https://orcid.org/0009-0006-4494-4558Farhad Uddin Mahmud2https://orcid.org/0009-0008-4243-5818Kanchon Kumar Bishnu3https://orcid.org/0009-0007-1811-3002Hadiur Rahman Nabil4https://orcid.org/0009-0005-4311-2875M. F. Mridha5https://orcid.org/0000-0001-5738-1631Md. Jakir Hossen6https://orcid.org/0000-0002-9978-7987School of Business, International American University, Los Angeles, CA, USASchool of Business, International American University, Los Angeles, CA, USASchool of Business, International American University, Los Angeles, CA, USACalifornia State University—Los Angeles, Los Angeles, CA, USADepartment of Computer Science and Engineering, American International University-Bangladesh, Dhaka, BangladeshDepartment of Computer Science and Engineering, American International University-Bangladesh, Dhaka, BangladeshCenter for Advanced Analytics (CAA), COE for Artificial Intelligence Faculty of Engineering & Technology (FET), Multimedia University, Melaka, MalaysiaPredictive maintenance is essential for ensuring the reliability and efficiency of wind energy systems. Traditional deep learning models for sensor fault detection rely solely on data-driven patterns, often lacking interpretability and robustness. This article proposes a Physics-Guided Bayesian Neural Network (PINN-BNN) model that integrates physics-informed learning with Bayesian inference to improve fault detection in wind turbines. The proposed approach enforces domain-specific constraints to ensure physically consistent predictions while quantifying uncertainty for risk-aware decision-making. The model is evaluated using a real-world wind turbine sensor dataset, achieving an accuracy of 97.6%, a recall of 91.8%, and an AUC-ROC of 0.987. The SHapley Additive exPlanations (SHAP) analysis reveals that gearbox temperature, blade vibration, and generator torque are the most critical features influencing failure predictions. Bayesian uncertainty estimation further improves interpretability by assigning confidence levels to each prediction. A comparative study with ten baseline models, including Long Short-Term Memory (LSTM), Transformer-based models, and traditional machine learning classifiers, demonstrates that the PINN-BNN model outperforms existing approaches while maintaining computational efficiency with a training time of 39.8 minutes and an inference time of 1.7 ms per sample. The integration of physics-informed learning ensures that the model generalizes well to varying environmental conditions, reducing false negatives and minimizing unexpected system failures. The proposed methodology presents a step toward interpretable and reliable predictive maintenance in wind energy systems.https://ieeexplore.ieee.org/document/11027711/Predictive maintenancewind turbinesphysics-informed neural networksBayesian inferenceuncertainty quantificationsensor fault detection
spellingShingle MD Azam Khan
Arifur Rahman
Farhad Uddin Mahmud
Kanchon Kumar Bishnu
Hadiur Rahman Nabil
M. F. Mridha
Md. Jakir Hossen
A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines
IEEE Open Journal of the Computer Society
Predictive maintenance
wind turbines
physics-informed neural networks
Bayesian inference
uncertainty quantification
sensor fault detection
title A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines
title_full A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines
title_fullStr A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines
title_full_unstemmed A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines
title_short A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines
title_sort physics guided bayesian neural network for sensor fault detection in wind turbines
topic Predictive maintenance
wind turbines
physics-informed neural networks
Bayesian inference
uncertainty quantification
sensor fault detection
url https://ieeexplore.ieee.org/document/11027711/
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