223
Views
0
CrossRef citations to date
0
Altmetric
Review Article

A review of artificial intelligence applications in wind turbine health monitoring

, &
Article: 2326296 | Received 11 Dec 2023, Accepted 18 Feb 2024, Published online: 08 May 2024

References

  • Abboud, R. 2021. Non-contact temperature measurement method integrated in the rotor of a rotating machine using fiber Bragg gratings.
  • Abdallah, I., V. Ntertimanis, C. Mylonas, K. Tatsis, E. Chatzi, N. Dervilis, W. Keith, and M. Eoghan. 2018. “Fault Diagnosis of Wind Turbine Structures Using Decision Tree Learning Algorithms with big Data.” Saf. Reliab. Soc. a Chang. World, 3053–3061.
  • Abdusamad, K. B., D. W. Gao, and E. Muljadi. 2013. “A Condition Monitoring System for Wind Turbine Generator Temperature by Applying Multiple Linear Regression Model.” In 2013 North Am. Power Symp., 1–8. IEEE.
  • Ab Wahab, M. N., S. Nefti-Meziani, and A. Atyabi. 2015. “A Comprehensive Review of Swarm Optimization Algorithms.” PLoS One 10: e0122827.
  • Ahmad, R., and S. Kamaruddin. 2012. “An Overview of Time-Based and Condition-Based Maintenance in Industrial Application.” Comput. Ind. Eng 63: 135–149.
  • Albano, M., L. Lino Ferreira, G. Di Orio, P. Maló, G. Webers, E. Jantunen, I. Gabilondo, M. Viguera, and G. Papa. 2020. “Advanced Sensor-Based Maintenance in Real-World Exemplary Cases.” Automatika 61: 537–553.
  • Astolfi, D., F. De Caro, and A. Vaccaro. 2023. “Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques.” Sensors 23: 5376.
  • Azar, K., Z. Hajiakhondi-Meybodi, and F. Naderkhani. 2022. “Semi-supervised Clustering-Based Method for Fault Diagnosis and Prognosis: A Case Study.” Reliab. Eng. Syst. Saf 222: 108405.
  • Bach-Andersen, M., B. Rømer-Odgaard, and O. Winther. 2018. “Deep Learning for Automated Drivetrain Fault Detection.” Wind Energy 21: 29–41.
  • Bai, X., Z. An, Y. Hou, and Q. Ma. 2017. “Health Assessment and Management of Wind Turbine Blade Based on the Fatigue Test Data.” Microelectron. Reliab 75: 205–214.
  • Ben Ali, J., L. Saidi, S. Harrath, E. Bechhoefer, and M. Benbouzid. 2018. “Online Automatic Diagnosis of Wind Turbine Bearings Progressive Degradations Under Real Experimental Conditions Based on Unsupervised Machine Learning.” Appl. Acoust 132: 167–181.
  • Benmessaoud, T., A. P. Marugán, K. Mohammedi, and F. P. G. Márquez. 2018. “Fuzzy Logic Applied to SCADA Systems.” In Proc. Elev. Int. Conf. Manag. Sci. Eng. Manag. 11, 749–757. Springer.
  • Bilendo, F., H. Badihi, N. Lu, P. Cambron, and B. Jiang. 2021. “An Intelligent Data-Driven Machine Learning Approach for Fault Detection of Wind Turbines.” In 2021 6th Int. Conf. Power Renew. Energy, 444–449. IEEE.
  • Bogue, R. 2013. “Sensors for Condition Monitoring: A Review of Technologies and Applications.” Sens. Rev 33: 295–299.
  • Brito, L. C., G. A. Susto, J. N. Brito, and M. A. V. Duarte. 2022. “An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery.” Mech. Syst. Signal Process 163: 108105.
  • Brusa, E., L. Cibrario, C. Delprete, and L. G. Di Maggio. 2023. “Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring.” Appl. Sci 13: 2038.
  • Catelani, M., L. Ciani, D. Galar, and G. Patrizi. 2020. “Optimizing Maintenance Policies for a yaw System Using Reliability-Centered Maintenance and Data-Driven Condition Monitoring.” IEEE Trans. Instrum. Meas 69: 6241–6249.
  • Chandrasekhar, K., N. Stevanovic, E. J. Cross, N. Dervilis, and K. Worden. 2021. “Damage Detection in Operational Wind Turbine Blades Using a new Approach Based on Machine Learning.” Renewable Energy 168: 1249–1264.
  • Chang, Y., J. Chen, C. Qu, and T. Pan. 2020. “Intelligent Fault Diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels.” Renewable Energy 153: 205–213.
  • Chatterjee, J., and N. Dethlefs. 2020. “Deep Learning with Knowledge Transfer for Explainable Anomaly Prediction in Wind Turbines.” Wind Energy 23: 1693–1710.
  • Chen, J., J. Li, W. Chen, Y. Wang, and T. Jiang. 2020. “Anomaly Detection for Wind Turbines Based on the Reconstruction of Condition Parameters Using Stacked Denoising Autoencoders.” Renewable Energy 147: 1469–1480.
  • Chen, P., Y. Li, K. Wang, M. J. Zuo, P. S. Heyns, and S. Baggeröhr. 2021aa. “A Threshold Self-Setting Condition Monitoring Scheme for Wind Turbine Generator Bearings Based on Deep Convolutional Generative Adversarial Networks.” Measurement 167: 108234.
  • Chen, H., H. Liu, X. Chu, Q. Liu, and D. Xue. 2021ba. “Anomaly Detection and Critical SCADA Parameters Identification for Wind Turbines Based on LSTM-AE Neural Network.” Renewable Energy 172: 829–840.
  • Chen, W., Y. Qiu, Y. Feng, Y. Li, and A. Kusiak. 2021bb. “Diagnosis of Wind Turbine Faults with Transfer Learning Algorithms.” Renewable Energy 163: 2053–2067.
  • Chen, L., G. Xu, Q. Zhang, and X. Zhang. 2019. “Learning Deep Representation of Imbalanced SCADA Data for Fault Detection of Wind Turbines.” Measurement 139: 370–379.
  • Cheng, J. C. P., W. Chen, K. Chen, and Q. Wang. 2020. “Data-driven Predictive Maintenance Planning Framework for MEP Components Based on BIM and IoT Using Machine Learning Algorithms.” Autom. Constr 112: 103087.
  • Cheng, F., L. Qu, and W. Qiao. 2017. “Fault Prognosis and Remaining Useful Life Prediction of Wind Turbine Gearboxes Using Current Signal Analysis.” IEEE Transactions on Sustainable Energy 9: 157–167.
  • Choe, D.-E., H.-C. Kim, and M.-H. Kim. 2021. “Sequence-based Modeling of Deep Learning with LSTM and GRU Networks for Structural Damage Detection of Floating Offshore Wind Turbine Blades.” Renewable Energy 174: 218–235.
  • Coronado, D., and J. Wenske. 2018. “Monitoring the Oil of Wind-Turbine Gearboxes: Main Degradation Indicators and Detection Methods.” Machines 6: 25.
  • Dahiya, R. 2018. “Condition Monitoring of Wind Turbine for Rotor Fault Detection Under non Stationary Conditions.” Ain Shams Eng. J 9: 2441–2452.
  • Danielsen, H. K., A. J. Carrasco, S. Fæster, K. V. Dahl, F. G. Guzmán, P. Sauvage, and G. Jacobs. 2019. “3D X-ray Computerized Tomography of White Etching Cracks (WEC).” Mater. Charact 150: 78–87.
  • Dao, P. B., W. J. Staszewski, T. Barszcz, and T. Uhl. 2018. “Condition Monitoring and Fault Detection in Wind Turbines Based on Cointegration Analysis of SCADA Data.” Renewable Energy 116: 107–122.
  • de Sá, F. P. G., R. de C. Coutinho, E. Ogasawara, D. Brandão, and R. F. Toso. 2023. “Wind Turbine Fault Detection: A Semi-Supervised Learning Approach with two Different Dimensionality Reduction Techniques.” Int. J. Innov. Comput. Appl 14: 67–77.
  • Dhiman, H. S., D. Deb, S. M. Muyeen, and I. Kamwa. 2021. “Wind Turbine Gearbox Anomaly Detection Based on Adaptive Threshold and Twin Support Vector Machines.” IEEE Trans. Energy Convers 36: 3462–3469.
  • Doan, D. D., E. Ramasso, V. Placet, S. Zhang, L. Boubakar, and N. Zerhouni. 2015. “An Unsupervised Pattern Recognition Approach for AE Data Originating from Fatigue Tests on Polymer–Composite Materials.” Mech. Syst. Signal Process 64: 465–478.
  • Dong, M., M. Sun, D. Song, L. Huang, J. Yang, and Y. H. Joo. 2022. “Real-time Detection of Wind Power Abnormal Data Based on Semi-Supervised Learning Robust Random Cut Forest.” Energy 257: 124761.
  • Durbhaka, G. K., B. Selvaraj, M. Mittal, T. Saba, A. Rehman, and L. M. Goyal. 2021. “Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms.” C. Mater. Contin 66: 2041–2059.
  • Elasha, F., S. Shanbr, X. Li, and D. Mba. 2019. “Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning.” Sensors 19: 3092.
  • Feng, T., Y. Liu, Q. Li, and Z. Ren. 2023. “Health Monitoring for Variable Pitch Systems of Wind Turbine Using Multi-Layer Perceptron Strategy.” In Adv. Guid. Navig. Control Proc. 2022 Int. Conf. Guid. Navig. Control, 248–254. Springer.
  • Feng, B., D. Zhang, Y. Si, X. Tian, and P. Qian. 2019. “A Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network.” In 2019 25th Int. Conf. Autom. Comput., 1–4. IEEE.
  • Fernandez-Gauna, B., M. Graña, J.-L. Osa-Amilibia, and X. Larrucea. 2022. “Actor-Critic Continuous State Reinforcement Learning for Wind-Turbine Control Robust Optimization.” Inf. Sci. (NY 591: 365–380.
  • Frankenstein, B., L. Schubert, N. Meyendorf, H. Friedmann, and C. Ebert. 2009. “Monitoring System of Wind Turbine Rotor Blades.” In Smart Sens. Phenomena, Technol. Networks, Syst., 260–271. SPIE.
  • Fu, J., J. Chu, P. Guo, and Z. Chen. 2019. “Condition Monitoring of Wind Turbine Gearbox Bearing Based on Deep Learning Model.” IEEE Access 7: 57078–57087.
  • Gao, Z., M. Chen, W. G. Guo, and J. Li. 2020. “Tool Wear Characterization and Monitoring with Hierarchical Spatio-Temporal Models for Micro-Friction Stir Welding.” J. Manuf. Process 56: 1353–1365.
  • Ghaheri, A., S. Shoar, M. Naderan, and S. S. Hoseini. 2015. “The Applications of Genetic Algorithms in Medicine.” Oman Med. J 30: 406.
  • Gielen, D., F. Boshell, D. Saygin, M. D. Bazilian, N. Wagner, and R. Gorini. 2019. “The Role of Renewable Energy in the Global Energy Transformation.” Energy Strateg. Rev 24: 38–50.
  • Gonzalo, A. P., T. Benmessaoud, M. Entezami, and F. P. G. Márquez. 2022. “Optimal Maintenance Management of Offshore Wind Turbines by Minimizing the Costs.” Sustain. Energy Technol. Assessments 52: 102230.
  • Han, T., C. Liu, L. Wu, S. Sarkar, and D. Jiang. 2019. “An Adaptive Spatiotemporal Feature Learning Approach for Fault Diagnosis in Complex Systems.” Mech. Syst. Signal Process 117: 170–187.
  • Huang, D., W.-A. Zhang, F. Guo, W. Liu, and X. Shi. 2021. “Wavelet Packet Decomposition-Based Multiscale CNN for Fault Diagnosis of Wind Turbine Gearbox.” IEEE Trans. Cybern.
  • Ibrahim, R., J. Weinert, and S. Watson. 2016. “Neural Networks for Wind Turbine Fault Detection via Current Signature Analysis.
  • Inturi, V., G. R. Sabareesh, K. Supradeepan, and P. K. Penumakala. 2019. “Integrated Condition Monitoring Scheme for Bearing Fault Diagnosis of a Wind Turbine Gearbox.” Journal of Vibration and Control 25: 1852–1865.
  • Jiang, G., H. He, J. Yan, and P. Xie. 2018. “Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox.” IEEE Trans. Ind. Electron 66: 3196–3207.
  • Jiang, N., X. Hu, and N. Li. 2020. “Graphical Temporal Semi-Supervised Deep Learning–Based Principal Fault Localization in Wind Turbine Systems.” Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng 234: 985–999.
  • Jiang, Y., T. Xia, D. Wang, X. Fang, and L. Xi. 2022a. “Adversarial Regressive Domain Adaptation Approach for Infrared Thermography-Based Unsupervised Remaining Useful Life Prediction.” IEEE Trans. Ind. Informatics 18: 7219–7229.
  • Jiang, Y., T. Xia, D. Wang, X. Fang, and L. Xi. 2022b. “Spatiotemporal Denoising Wavelet Network for Infrared Thermography-Based Machine Prognostics Integrating Ensemble Uncertainty.” Mech. Syst. Signal Process 173: 109014.
  • Jiang, G., P. Xie, H. He, and J. Yan. 2017. “Wind Turbine Fault Detection Using a Denoising Autoencoder with Temporal Information.” IEEE/ASME Transactions on Mechatronics 23: 89–100.
  • Jiménez, A. A., F. P. G. Márquez, V. B. Moraleda, and C. Q. G. Muñoz. 2019. “Linear and Nonlinear Features and Machine Learning for Wind Turbine Blade ice Detection and Diagnosis.” Renewable Energy 132: 1034–1048.
  • Jiménez, A. A., L. Zhang, C. Q. G. Muñoz, and F. P. G. Márquez. 2020. “Maintenance Management Based on Machine Learning and Nonlinear Features in Wind Turbines.” Renewable Energy 146: 316–328.
  • Jin, X., Z. Xu, and W. Qiao. 2020. “Condition Monitoring of Wind Turbine Generators Using SCADA Data Analysis.” IEEE Transactions on Sustainable Energy 12: 202–210.
  • Jing, H., and C. Zhao. 2022. “Adjustable Piecewise Regression Strategy Based Wind Turbine Power Forecasting for Probabilistic Condition Monitoring.” Sustain. Energy Technol. Assessments 52: 102013.
  • Jing, L., M. Zhao, P. Li, and X. Xu. 2017. “A Convolutional Neural Network Based Feature Learning and Fault Diagnosis Method for the Condition Monitoring of Gearbox.” Measurement 111: 1–10.
  • Joshuva, A., R. S. Kumar, S. Sivakumar, G. Deenadayalan, and R. Vishnuvardhan. 2020. “An Insight on VMD for Diagnosing Wind Turbine Blade Faults Using C4.5 as Feature Selection and Discriminating Through Multilayer Perceptron.” Alexandria Engineering Journal 59: 3863–3879.
  • Joshuva, A., and V. Sugumaran. 2020. “A Lazy Learning Approach for Condition Monitoring of Wind Turbine Blade Using Vibration Signals and Histogram Features.” Measurement 152: 107295.
  • Keleko, A. T., B. Kamsu-Foguem, R. H. Ngouna, and A. Tongne. 2023. “Health Condition Monitoring of a Complex Hydraulic System Using Deep Neural Network and DeepSHAP Explainable XAI.” Adv. Eng. Softw 175: 103339.
  • Koh, P. W., and P. Liang. 2017. “Understanding Black-Box Predictions via Influence Functions.” In Int. Conf. Mach. Learn., 1885–1894. PMLR.
  • Kong, Z., B. Tang, L. Deng, W. Liu, and Y. Han. 2020. “Condition Monitoring of Wind Turbines Based on Spatio-Temporal Fusion of SCADA Data by Convolutional Neural Networks and Gated Recurrent Units.” Renewable Energy 146: 760–768.
  • Kouadri, A., M. Hajji, M.-F. Harkat, K. Abodayeh, M. Mansouri, H. Nounou, and M. Nounou. 2020. “Hidden Markov Model Based Principal Component Analysis for Intelligent Fault Diagnosis of Wind Energy Converter Systems.” Renewable Energy 150: 598–606.
  • Lee, J., and N.-W. Cho. 2016. “Fast Outlier Detection Using a Grid-Based Algorithm.” PLoS One 11: e0165972.
  • Lei, J., C. Liu, and D. Jiang. 2019. “Fault Diagnosis of Wind Turbine Based on Long Short-Term Memory Networks.” Renewable Energy 133: 422–432.
  • Li, Y., W. Jiang, G. Zhang, and L. Shu. 2021a. “Wind Turbine Fault Diagnosis Based on Transfer Learning and Convolutional Autoencoder with Small-Scale Data.” Renewable Energy 171: 103–115.
  • Li, G., C. Wang, D. Zhang, and G. Yang. 2021b. “An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring.” Sensors 21: 5654.
  • Li, N., X. Yun, Q. Han, B. Wen, and J. Zhai. 2023. “Characterization Method of Rolling Bearing Operation State Based on Feature Information Fusion.” J. Mech. Sci. Technol 37: 1197–1205.
  • Liang, L., F. Liu, M. Li, K. He, and G. Xu. 2016. “Feature Selection for Machine Fault Diagnosis Using Clustering of non-Negation Matrix Factorization.” Measurement 94: 295–305.
  • Liang, X., G. Wang, M. R. Min, Y. Qi, and Z. Han. 2019. “A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction.” In Proc. 2019 SIAM Int. Conf. Data Min., SIAM, 100–108.
  • Lin, Z., X. Liu, and M. Collu. 2020. “Wind Power Prediction Based on High-Frequency SCADA Data Along with Isolation Forest and Deep Learning Neural Networks.” Int. J. Electr. Power Energy Syst 118: 105835.
  • Liu, Y., H. Cheng, X. Kong, Q. Wang, and H. Cui. 2019. “Intelligent Wind Turbine Blade Icing Detection Using Supervisory Control and Data Acquisition Data and Ensemble Deep Learning.” Energy Sci. Eng 7: 2633–2645.
  • Liu, Y., T. Wang, and F. Chu. 2024. “Hybrid Machine Condition Monitoring Based on Interpretable Dual Tree Methods Using Wasserstein Metrics.” Expert Syst. Appl 235: 121104.
  • Liu, P., D. Xu, J. Li, Z. Chen, S. Wang, J. Leng, R. Zhu, L. Jiao, W. Liu, and Z. Li. 2020. “Damage Mode Identification of Composite Wind Turbine Blade Under Accelerated Fatigue Loads Using Acoustic Emission and Machine Learning.” Struct. Heal. Monit 19: 1092–1103.
  • Liu, J., G. Yang, X. Li, Q. Wang, Y. He, and X. Yang. 2023. “Wind Turbine Anomaly Detection Based on SCADA: A Deep Autoencoder Enhanced by Fault Instances.” ISA Transactions.
  • Liu, H., C. Yu, and C. Yu. 2021. “A New Hybrid Model Based on Secondary Decomposition, Reinforcement Learning and SRU Network for Wind Turbine Gearbox oil Temperature Forecasting.” Measurement 178: 109347.
  • Mahmoud, T., Z. Y. Dong, and J. Ma. 2018. “An Advanced Approach for Optimal Wind Power Generation Prediction Intervals by Using Self-Adaptive Evolutionary Extreme Learning Machine.” Renewable Energy 126: 254–269.
  • Manobel, B., F. Sehnke, J. A. Lazzús, I. Salfate, M. Felder, and S. Montecinos. 2018. “Wind Turbine Power Curve Modeling Based on Gaussian Processes and Artificial Neural Networks.” Renewable Energy 125: 1015–1020.
  • Mao, W., N. Dai, and H. Li. 2019. “Economic Dispatch of Microgrid Considering Fuzzy Control Based Storage Battery Charging and Discharging.” J. Electr. Syst 15.
  • Márquez, F. P. G., A. M. Tobias, J. M. P. Pérez, and M. Papaelias. 2012. “Condition Monitoring of Wind Turbines: Techniques and Methods.” Renewable Energy 46: 169–178.
  • McCrory, J. P., S. K. Al-Jumaili, D. Crivelli, M. R. Pearson, M. J. Eaton, C. A. Featherston, M. Guagliano, K. M. Holford, and R. Pullin. 2015. “Damage Classification in Carbon Fibre Composites Using Acoustic Emission: A Comparison of Three Techniques.” Compos. Part B Eng 68: 424–430.
  • Mehlan, F. C., and A. R. Nejad. 2023. “Rotor Imbalance Detection and Diagnosis in Floating Wind Turbines by Means of Drivetrain Condition Monitoring.” Renewable Energy.
  • Murgia, A., R. Verbeke, E. Tsiporkova, L. Terzi, and D. Astolfi. 2023. “Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis Through Temperature Data Analysis.” Energies 16: 620.
  • Nam, S., and J. Hur. 2019. “A Hybrid Spatio-Temporal Forecasting of Solar Generating Resources for Grid Integration.” Energy 177: 503–510.
  • Ogaili, A. A. F., A. A. Jaber, and M. N. Hamzah. 2023. “Statistically Optimal Vibration Feature Selection for Fault Diagnosis in Wind Turbine Blade.” Int. J. Renew. Energy Res 13: 1082–1092.
  • Orozco, R., S. Sheng, and C. Phillips. 2018. “Diagnostic Models for Wind Turbine Gearbox Components Using Scada Time Series Data.” In 2018 IEEE Int. Conf. Progn. Heal. Manag., 1–9. IEEE.
  • Ou, Y., K. E. Tatsis, V. K. Dertimanis, M. D. Spiridonakos, and E. N. Chatzi. 2021. “Vibration-Based Monitoring of a Small-Scale Wind Turbine Blade Under Varying Climate Conditions. Part I: An Experimental Benchmark.” Struct. Control Heal. Monit 28: e2660.
  • Pandit, R., and D. Infield. 2018. “Gaussian Process Operational Curves for Wind Turbine Condition Monitoring.” Energies 11: 1631.
  • Pandit, R. K., and D. Infield. 2019. “Comparative Assessments of Binned and Support Vector Regression-Based Blade Pitch Curve of a Wind Turbine for the Purpose of Condition Monitoring.” Int. J. Energy Environ. Eng 10: 181–188.
  • Peng, J., A. Kimmig, Z. Niu, J. Wang, X. Liu, D. Wang, and J. Ovtcharova. 2022. “Wind Turbine Failure Prediction and Health Assessment Based on Adaptive Maximum Mean Discrepancy.” Int. J. Electr. Power Energy Syst 134: 107391.
  • Perez-Sanjines, F., C. Peeters, T. Verstraeten, J. Antoni, A. Nowé, and J. Helsen. 2023. “Fleet-Based Early Fault Detection of Wind Turbine Gearboxes Using Physics-Informed Deep Learning Based on Cyclic Spectral Coherence.” Mech. Syst. Signal Process 185: 109760.
  • Pozo, F., Y. Vidal, and Ó Salgado. 2018. “Wind Turbine Condition Monitoring Strategy Through Multiway PCA and Multivariate Inference.” Energies 11: 749.
  • Qian, P., X. Tian, J. Kanfoud, J. L. Y. Lee, and T.-H. Gan. 2019. “A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network.” Energies 12: 3411.
  • Qiao, H., T. Wang, P. Wang, S. Qiao, and L. Zhang. 2018. “A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series.” Sensors 18: 2932.
  • Qu, F., J. Liu, H. Zhu, and B. Zhou. 2020. “Wind Turbine Fault Detection Based on Expanded Linguistic Terms and Rules Using Non-Singleton Fuzzy Logic.” Applied Energy 262: 114469.
  • Reddy, A., V. Indragandhi, L. Ravi, and V. Subramaniyaswamy. 2019. “Detection of Cracks and Damage in Wind Turbine Blades Using Artificial Intelligence-Based Image Analytics.” Measurement 147: 106823.
  • Ren, H., W. Liu, M. Shan, and X. Wang. 2019. “A new Wind Turbine Health Condition Monitoring Method Based on VMD-MPE and Feature-Based Transfer Learning.” Measurement 148: 106906.
  • Rodriguez, P. C., P. Marti-Puig, C. F. Caiafa, M. Serra-Serra, J. Cusidó, and J. Solé-Casals. 2023. “Exploratory Analysis of SCADA Data from Wind Turbines Using the K-Means Clustering Algorithm for Predictive Maintenance Purposes.” Machines 11: 270.
  • Rogers, T. J., P. Gardner, N. Dervilis, K. Worden, A. E. Maguire, E. Papatheou, and E. J. Cross. 2020. “Probabilistic Modelling of Wind Turbine Power Curves with Application of Heteroscedastic Gaussian Process Regression.” Renewable Energy 148: 1124–1136.
  • Saenz-Aguirre, A., E. Zulueta, U. Fernandez-Gamiz, A. Ulazia, and D. Teso-Fz-Betono. 2020. “Performance Enhancement of the Artificial Neural Network–Based Reinforcement Learning for Wind Turbine Yaw Control.” Wind Energy 23: 676–690.
  • Salakhutdinov, R., and H. Larochelle. 2010. “Efficient Learning of Deep Boltzmann Machines.” In Proc. Thirteen. Int. Conf. Artif. Intell. Stat., 693–700. JMLR Workshop and Conference Proceedings.
  • Sales-Setién, E., and I. Peñarrocha-Alós. 2020. “Robust Estimation and Diagnosis of Wind Turbine Pitch Misalignments at a Wind Farm Level.” Renewable Energy 146: 1746–1765.
  • Sanakkayala, D. C., V. Varadarajan, N. Kumar, G. Soni, P. Kamat, S. Kumar, S. Patil, and K. Kotecha. 2022. “Explainable AI for Bearing Fault Prognosis Using Deep Learning Techniques.” Micromachines 13: 1471.
  • Scheu, M. N., L. Tremps, U. Smolka, A. Kolios, and F. Brennan. 2019. “A Systematic Failure Mode Effects and Criticality Analysis for Offshore Wind Turbine Systems Towards Integrated Condition Based Maintenance Strategies.” Ocean Engineering 176: 118–133.
  • Shaheen, B. W., and I. Németh. 2023. “Performance Monitoring of Wind Turbines Gearbox Utilising Artificial Neural Networks—Steps Toward Successful Implementation of Predictive Maintenance Strategy.” Processes 11: 269.
  • Shao, H., H. Jiang, H. Zhang, W. Duan, T. Liang, and S. Wu. 2018. “Rolling Bearing Fault Feature Learning Using Improved Convolutional Deep Belief Network with Compressed Sensing.” Mech. Syst. Signal Process 100: 743–765.
  • Shayeghi, H., and Y. Hashemi. 2015. “Application of Fuzzy Decision-Making Based on INSGA-II to Designing PV–Wind Hybrid System.” Eng. Appl. Artif. Intell 45: 1–17.
  • Sierra-Garcia, J. E., and M. Santos. 2020a. “Wind Turbine Pitch Control First Approach Based on Reinforcement Learning.” In Intell. Data Eng. Autom. Learn. 2020 21st Int. Conf. Guimaraes, Port. Novemb. 4–6, 2020, Proceedings, Part II 21, 260–268. Springer.
  • Sierra-Garcia, J. E., and M. Santos. 2020b. “Exploring Reward Strategies for Wind Turbine Pitch Control by Reinforcement Learning.” Appl. Sci 10: 7462.
  • Sierra-Garcia, J. E., M. Santos, and R. Pandit. 2022. “Wind Turbine Pitch Reinforcement Learning Control Improved by PID Regulator and Learning Observer.” Eng. Appl. Artif. Intell 111: 104769.
  • Singh, S. S., and E. Fernandez. 2017. “Impact of Wind Turbine Generator for on the Reliability and Economics of a Remote WTG System.” In 2017 IEEE Int. Conf. Power, Control. Signals Instrum. Eng., 627–631. IEEE.
  • Sivakumar, A., and S. Vaithiyanathan. 2021. “Vibration Based Data Analysis of Single Acting Compressor Through Condition Monitoring and Multilayer Perceptron–A Machine Learning Classifier.” In IOP Conf. Ser. Mater. Sci. Eng., 12032. IOP Publishing.
  • Soman, R. 2022. “Multi-Objective Optimization for Joint Actuator and Sensor Placement for Guided Waves Based Structural Health Monitoring Using Fibre Bragg Grating Sensors.” Ultrasonics 119: 106605.
  • Song, D., J. Yang, X. Fan, Y. Liu, A. Liu, G. Chen, and Y. H. Joo. 2018. “Maximum Power Extraction for Wind Turbines Through a Novel Yaw Control Solution Using Predicted Wind Directions.” Energy Convers. Manag 157: 587–599.
  • Strömbergsson, D., P. Marklund, K. Berglund, J. Saari, and A. Thomson. 2019. “Mother Wavelet Selection in the Discrete Wavelet Transform for Condition Monitoring of Wind Turbine Drivetrain Bearings.” Wind Energy 22: 1581–1592.
  • Sun, Z., and H. Sun. 2019. “Stacked Denoising Autoencoder with Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components.” IEEE Access 7: 13078–13091.
  • Surucu, O., S. A. Gadsden, and J. Yawney. 2023. “Condition Monitoring Using Machine Learning: A Review of Theory, Applications, and Recent Advances.” Expert Syst. Appl 221: 119738.
  • Tang, J., S. Soua, C. Mares, and T.-H. Gan. 2017. “A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades.” Sensors 17: 2507.
  • Thirunavukkarasu, M., Y. Sawle, and H. Lala. 2023. “A Comprehensive Review on Optimization of Hybrid Renewable Energy Systems Using Various Optimization Techniques.” Renew. Sustain. Energy Rev 176: 113192.
  • Tian, X., Y. Jiang, C. Liang, C. Liu, Y. Ying, H. Wang, D. Zhang, and P. Qian. 2022. “A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network.” Energies 15: 6717.
  • Turi, J. A., J. Rosak-Szyrocka, M. Mansoor, H. Asif, A. Nazir, and D. Balsalobre-Lorente. 2022. “Assessing Wind Energy Projects Potential in Pakistan: Challenges and Way Forward.” Energies 15: 9014.
  • Vidal, Y., F. Pozo, and C. Tutivén. 2018. “Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data.” Energies 11: 3018.
  • Wang, M.-H., S.-D. Lu, C.-C. Hsieh, and C.-C. Hung. 2022. “Fault Detection of Wind Turbine Blades Using Multi-Channel CNN.” Sustainability 14: 1781.
  • Wang, L.-M., and Y.-M. Shao. 2018. “Crack Fault Classification for Planetary Gearbox Based on Feature Selection Technique and K-Means Clustering Method, Chinese J.” Mech. Eng 31: 1–11.
  • Wang, H., H. Wang, G. Jiang, J. Li, and Y. Wang. 2019a. “Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling.” Energies 12: 984.
  • Wang, Z., L. Yao, Y. Cai, and J. Zhang. 2020. “Mahalanobis Semi-Supervised Mapping and Beetle Antennae Search Based Support Vector Machine for Wind Turbine Rolling Bearings Fault Diagnosis.” Renewable Energy 155: 1312–1327.
  • Wang, Y., H. Ye, T. Zhang, and H. Zhang. 2019b. “A Data Mining Method Based on Unsupervised Learning and Spatiotemporal Analysis for Sheath Current Monitoring.” Neurocomputing 352: 54–63.
  • Wang, J., W. Zhou, X. Ren, M. Su, and J. Liu. 2023. “A Waveform-Based Clustering and Machine Learning Method for Damage Mode Identification in CFRP Laminates.” Compos. Struct 312: 116875.
  • Wei, L., Z. Qian, and H. Zareipour. 2019. “Wind Turbine Pitch System Condition Monitoring and Fault Detection Based on Optimized Relevance Vector Machine Regression.” IEEE Transactions on Sustainable Energy 11: 2326–2336.
  • Worms, K., C. Klamouris, F. Wegh, L. Meder, D. Volkmer, S. P. Philipps, S. K. Reichmuth, H. Helmers, A. Kunadt, and J. Vourvoulakis. 2017. “Reliable and Lightning-Safe Monitoring of Wind Turbine Rotor Blades Using Optically Powered Sensors.” Wind Energy 20: 345–360.
  • Wu, Y., and X. Ma. 2022. “A Hybrid LSTM-KLD Approach to Condition Monitoring of Operational Wind Turbines.” Renewable Energy 181: 554–566.
  • Wu, B., and B. M. Wilamowski. 2016. “A Fast Density and Grid Based Clustering Method for Data with Arbitrary Shapes and Noise.” IEEE Trans. Ind. Informatics 13: 1620–1628.
  • Xiang, L., P. Wang, X. Yang, A. Hu, and H. Su. 2021. “Fault Detection of Wind Turbine Based on SCADA Data Analysis Using CNN and LSTM with Attention Mechanism.” Measurement 175: 109094.
  • Xiao, X., J. Liu, D. Liu, Y. Tang, J. Dai, and F. Zhang. 2021. “SSAE-MLP: Stacked Sparse Autoencoders-Based Multi-Layer Perceptron for Main Bearing Temperature Prediction of Large-Scale Wind Turbines.” Concurr. Comput. Pract. Exp 33: e6315.
  • Xiao, X., J. Liu, D. Liu, Y. Tang, and F. Zhang. 2022. “Condition Monitoring of Wind Turbine Main Bearing Based on Multivariate Time Series Forecasting.” Energies 15: 1951.
  • Xu, D., P. F. Liu, Z. P. Chen, J. X. Leng, and L. Jiao. 2020. “Achieving Robust Damage Mode Identification of Adhesive Composite Joints for Wind Turbine Blade Using Acoustic Emission and Machine Learning.” Compos. Struct 236: 111840.
  • Xu, D., C. Wen, and J. Liu. 2019. “Wind Turbine Blade Surface Inspection Based on Deep Learning and UAV-Taken Images.” J. Renew. Sustain. Energy 11: 53305.
  • Yang, W. 2016. “Condition Monitoring of Offshore Wind Turbines.” In Offshore Wind Farms, 543–572. Elsevier.
  • Yang, W., R. Court, and J. Jiang. 2013. “Wind Turbine Condition Monitoring by the Approach of SCADA Data Analysis.” Renewable Energy 53: 365–376.
  • Yang, W., R. Court, P. J. Tavner, and C. J. Crabtree. 2011. “Bivariate Empirical Mode Decomposition and Its Contribution to Wind Turbine Condition Monitoring.” J. Sound Vib 330: 3766–3782.
  • Yang, R., Y. He, and H. Zhang. 2016. “Progress and Trends in Nondestructive Testing and Evaluation for Wind Turbine Composite Blade.” Renew. Sustain. Energy Rev 60: 1225–1250.
  • Yang, W., C. Liu, and D. Jiang. 2018. “An Unsupervised Spatiotemporal Graphical Modeling Approach for Wind Turbine Condition Monitoring.” Renewable Energy 127: 230–241.
  • Yang, C., J. Liu, Y. Zeng, and G. Xie. 2019. “Real-Time Condition Monitoring and Fault Detection of Components Based on Machine-Learning Reconstruction Model.” Renewable Energy 133: 433–441.
  • Yang, B., and D. Sun. 2013. “Testing, Inspecting and Monitoring Technologies for Wind Turbine Blades: A Survey.” Renew. Sustain. Energy Rev 22: 515–526.
  • Yang, L., and Z. Zhang. 2020. “Wind Turbine Gearbox Failure Detection Based on SCADA Data: A Deep Learning-Based Approach.” IEEE Trans. Instrum. Meas 70: 1–11.
  • Yang, X., Y. Zhang, W. Lv, and D. Wang. 2021. “Image Recognition of Wind Turbine Blade Damage Based on a Deep Learning Model with Transfer Learning and an Ensemble Learning Classifier.” Renewable Energy 163: 386–397.
  • Yeh, C.-H., M.-H. Lin, C.-H. Lin, C.-E. Yu, and M.-J. Chen. 2019. “Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine.” Sensors 19: 1671.
  • Yeter, B., Y. Garbatov, and C. G. Soares. 2022. “Life-Extension Classification of Offshore Wind Assets Using Unsupervised Machine Learning.” Reliab. Eng. Syst. Saf 219: 108229.
  • Yu, X. 2020. Modelling Offshore Wind Farm Operation and Maintenance: The Benefits of Condition Monitoring. Cambridge Scholars Publishing.
  • Yu, L., J. Qu, F. Gao, and Y. Tian. 2019. “A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM.” Shock and Vibration 2019.
  • Yu, X., B. Tang, and K. Zhang. 2021. “Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks.” IEEE Trans. Instrum. Meas 70: 1–14.
  • Zeng, X. J., M. Yang, and Y. F. Bo. 2020. “Gearbox oil Temperature Anomaly Detection for Wind Turbine Based on Sparse Bayesian Probability Estimation.” Int. J. Electr. Power Energy Syst 123: 106233.
  • Zhang, J., G. Cosma, and J. Watkins. 2021a. “Image Enhanced Mask r-cnn: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification, J.” Imaging 7: 46.
  • Zhang, Y., M. Li, Z. Y. Dong, and K. Meng. 2019. “Probabilistic Anomaly Detection Approach for Data-Driven Wind Turbine Condition Monitoring.” CSEE J. Power Energy Syst 5: 149–158.
  • Zhang, G., Y. Li, W. Jiang, and L. Shu. 2022. “A Fault Diagnosis Method for Wind Turbines with Limited Labeled Data Based on Balanced Joint Adaptive Network.” Neurocomputing 481: 133–153.
  • Zhang, L., B. Wang, P. Liang, X. Yuan, and N. Li. 2023. “Semi-Supervised Fault Diagnosis of Gearbox Based on Feature pre-Extraction Mechanism and Improved Generative Adversarial Networks Under Limited Labeled Samples and Noise Environment.” Adv. Eng. Informatics 58: 102211.
  • Zhang, D., Y. Yang, and H. Fang. 2021b. “A Comparative Study for Multilayer Perception Versus Convolutional Neural Network Based On the Wind Turbine Benchmark Model.” In 2021 33rd Chinese Control Decis. Conf., 3645–3649. IEEE.
  • Zhao, B., C. Cheng, Z. Peng, X. Dong, and G. Meng. 2020. “Detecting the Early Damages in Structures with Nonlinear Output Frequency Response Functions and the CNN-LSTM Model.” IEEE Trans. Instrum. Meas 69: 9557–9567.
  • Zhao, X., and M. Jia. 2020. “A Novel Unsupervised Deep Learning Network for Intelligent Fault Diagnosis of Rotating Machinery.” Struct. Heal. Monit 19: 1745–1763.
  • Zhao, H., H. Liu, W. Hu, and X. Yan. 2018. “Anomaly Detection and Fault Analysis of Wind Turbine Components Based on Deep Learning Network.” Renewable Energy 127: 825–834.
  • Zhou, K., E. Diehl, and J. Tang. 2023. “Deep Convolutional Generative Adversarial Network with Semi-Supervised Learning Enabled Physics Elucidation for Extended Gear Fault Diagnosis Under Data Limitations.” Mech. Syst. Signal Process 185: 109772.
  • Zhu, A., Q. Zhao, T. Yang, L. Zhou, and B. Zeng. 2023. “Condition Monitoring of Wind Turbine Based on Deep Learning Networks and Kernel Principal Component Analysis.” Comput. Electr. Eng 105: 108538.
  • Zitrou, A., T. Bedford, and L. Walls. 2016. “A Model for Availability Growth with Application to New Generation Offshore Wind Farms.” Reliab. Eng. Syst. Saf 152: 83–94.