Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks: Recent Advances and Key Insights
Wind energy conversion systems (WECSs) play a vital role in the transition to sustainable energy, requiring continuous advancements in reliability, efficiency, and predictive maintenance. This paper provides a comprehensive review of Bayesian Networks (BNs) as a robust probabilistic framework for en...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11036713/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849706128734158848 |
|---|---|
| author | Fatemeh Salboukh Yashar Mousavi Ibrahim Beklan Kucukdemiral Afef Fekih Umit Cali |
| author_facet | Fatemeh Salboukh Yashar Mousavi Ibrahim Beklan Kucukdemiral Afef Fekih Umit Cali |
| author_sort | Fatemeh Salboukh |
| collection | DOAJ |
| description | Wind energy conversion systems (WECSs) play a vital role in the transition to sustainable energy, requiring continuous advancements in reliability, efficiency, and predictive maintenance. This paper provides a comprehensive review of Bayesian Networks (BNs) as a robust probabilistic framework for enhancing fault detection, risk assessment, and condition monitoring in WECSs. By integrating diverse data sources—including supervisory control and data acquisition (SCADA) systems, sensor networks, and environmental monitoring tools—BNs facilitate predictive maintenance, improve failure diagnostics, and extend turbine lifespan through adaptive learning. Their capability to quantify uncertainty and model complex dependencies makes them particularly effective in addressing operational and failure uncertainties, ensuring reliable energy generation under variable environmental conditions. The paper further examines key implementation challenges, including computational demands, data integration complexities, and the need for high-quality datasets to refine probabilistic models. Future research directions focus on hybridizing BNs with deep learning (DL) and reinforcement learning (RL), incorporating real-time sensor data for adaptive reliability analysis, and developing scalable, computationally efficient frameworks. Given the rapid advancements in machine learning and data-driven methodologies over the past five years, this review highlights the evolving role of BNs in modernizing WECS operations, emphasizing the necessity of an updated perspective to address emerging challenges and opportunities in wind energy systems. |
| format | Article |
| id | doaj-art-eb2cdfd4fe2b45beb13c43ca541310d9 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eb2cdfd4fe2b45beb13c43ca541310d92025-08-20T03:16:17ZengIEEEIEEE Access2169-35362025-01-011310447210450010.1109/ACCESS.2025.357993911036713Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks: Recent Advances and Key InsightsFatemeh Salboukh0https://orcid.org/0009-0009-5183-6549Yashar Mousavi1https://orcid.org/0000-0002-6718-3599Ibrahim Beklan Kucukdemiral2https://orcid.org/0000-0003-0174-5680Afef Fekih3https://orcid.org/0000-0003-4522-502XUmit Cali4https://orcid.org/0000-0002-6402-0479Department of Engineering and Applied Science, University of Massachusetts Dartmouth, Dartmouth, MA, USADepartment of Engineering, School of Science and Engineering, Glasgow Caledonian University, Glasgow, U.K.Department of Engineering, School of Science and Engineering, Glasgow Caledonian University, Glasgow, U.K.Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, Lafayette, LA, USASchool of Physics, Engineering and Technology, University of York, York, U.K.Wind energy conversion systems (WECSs) play a vital role in the transition to sustainable energy, requiring continuous advancements in reliability, efficiency, and predictive maintenance. This paper provides a comprehensive review of Bayesian Networks (BNs) as a robust probabilistic framework for enhancing fault detection, risk assessment, and condition monitoring in WECSs. By integrating diverse data sources—including supervisory control and data acquisition (SCADA) systems, sensor networks, and environmental monitoring tools—BNs facilitate predictive maintenance, improve failure diagnostics, and extend turbine lifespan through adaptive learning. Their capability to quantify uncertainty and model complex dependencies makes them particularly effective in addressing operational and failure uncertainties, ensuring reliable energy generation under variable environmental conditions. The paper further examines key implementation challenges, including computational demands, data integration complexities, and the need for high-quality datasets to refine probabilistic models. Future research directions focus on hybridizing BNs with deep learning (DL) and reinforcement learning (RL), incorporating real-time sensor data for adaptive reliability analysis, and developing scalable, computationally efficient frameworks. Given the rapid advancements in machine learning and data-driven methodologies over the past five years, this review highlights the evolving role of BNs in modernizing WECS operations, emphasizing the necessity of an updated perspective to address emerging challenges and opportunities in wind energy systems.https://ieeexplore.ieee.org/document/11036713/Wind energy conversion systemsBayesian networkscondition monitoringreliability assessmentpredictive maintenance |
| spellingShingle | Fatemeh Salboukh Yashar Mousavi Ibrahim Beklan Kucukdemiral Afef Fekih Umit Cali Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks: Recent Advances and Key Insights IEEE Access Wind energy conversion systems Bayesian networks condition monitoring reliability assessment predictive maintenance |
| title | Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks: Recent Advances and Key Insights |
| title_full | Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks: Recent Advances and Key Insights |
| title_fullStr | Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks: Recent Advances and Key Insights |
| title_full_unstemmed | Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks: Recent Advances and Key Insights |
| title_short | Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks: Recent Advances and Key Insights |
| title_sort | reliability assessment and condition monitoring of wind energy conversion systems using bayesian networks recent advances and key insights |
| topic | Wind energy conversion systems Bayesian networks condition monitoring reliability assessment predictive maintenance |
| url | https://ieeexplore.ieee.org/document/11036713/ |
| work_keys_str_mv | AT fatemehsalboukh reliabilityassessmentandconditionmonitoringofwindenergyconversionsystemsusingbayesiannetworksrecentadvancesandkeyinsights AT yasharmousavi reliabilityassessmentandconditionmonitoringofwindenergyconversionsystemsusingbayesiannetworksrecentadvancesandkeyinsights AT ibrahimbeklankucukdemiral reliabilityassessmentandconditionmonitoringofwindenergyconversionsystemsusingbayesiannetworksrecentadvancesandkeyinsights AT afeffekih reliabilityassessmentandconditionmonitoringofwindenergyconversionsystemsusingbayesiannetworksrecentadvancesandkeyinsights AT umitcali reliabilityassessmentandconditionmonitoringofwindenergyconversionsystemsusingbayesiannetworksrecentadvancesandkeyinsights |