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...
Saved in:
Main Authors: | , , , , , , |
---|---|
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/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
_version_ | 1839640051611336704 |
---|---|
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. |
format | Article |
id | doaj-art-c4d7baf41b724f7ab96b69c68ab5c33e |
institution | Matheson Library |
issn | 2644-1268 |
language | English |
publishDate | 2025-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Open Journal of the Computer Society |
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/ |
work_keys_str_mv | AT mdazamkhan aphysicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT arifurrahman aphysicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT farhaduddinmahmud aphysicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT kanchonkumarbishnu aphysicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT hadiurrahmannabil aphysicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT mfmridha aphysicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT mdjakirhossen aphysicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT mdazamkhan physicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT arifurrahman physicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT farhaduddinmahmud physicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT kanchonkumarbishnu physicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT hadiurrahmannabil physicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT mfmridha physicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines AT mdjakirhossen physicsguidedbayesianneuralnetworkforsensorfaultdetectioninwindturbines |