{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T04:08:04Z","timestamp":1782965284502,"version":"3.54.5"},"reference-count":46,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T00:00:00Z","timestamp":1687651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFE0104800"],"award-info":[{"award-number":["2019YFE0104800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Condition-monitoring and anomaly-detection methods used for the assessment of wind turbines are key to reducing operation and maintenance (O&amp;M) cost and improving their reliability. In this study, based on the sparrow search algorithm (SSA), bidirectional long short-term memory networks with a self-attention mechanism (SABiLSTM), and a binary segmentation changepoint detection algorithm (BinSegCPD), a condition-monitoring method (SSA-SABiLSTM-BinSegCPD, SSD) used for wind turbines is proposed. Specifically, the self-attention mechanism, which can mine the nonlinear dynamic characteristics and spatial\u2013temporal features inherent in the SCADA time series, was introduced into a two-layer BiLSTM network to establish a normal-behavior model for wind turbine key components. Then, as a result of the advantages of searching precision and convergence rate methods, the sparrow search algorithm was employed to optimize the constructed SABiLSTM model. Moreover, the BinSegCPD algorithm was applied to the predicted residual sequence to achieve the automatic identification of deterioration conditions for wind turbines. Case studies conducted on multiple wind turbines located in south China showed that the established SSA-SABiLSTM model was superior to other contrast models, achieving a better prediction precision in terms of RMSE, MAE, MAPE, and R2. The MAE, RMSE, and MAPE of SSA-SABiLSTM were 0.2543 \u00b0C, 0.3412 \u00b0C, and 0.0069, which were 47.23%, 42.19%, and 53.38% lower than those of SABiLSTM, respectively. The R2 of SABiLSTM was 0.9731, which was 4.6% higher than that of SABiLSTM. The proposed SSD method can detect deterioration conditions 47\u2013120 h in advance and trigger fault alarm signals approximately 36 h ahead of the actual failure time.<\/jats:p>","DOI":"10.3390\/s23135873","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T05:28:02Z","timestamp":1687757282000},"page":"5873","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Wind Turbine Condition Monitoring Using the SSA-Optimized Self-Attention BiLSTM Network and Changepoint Detection Algorithm"],"prefix":"10.3390","volume":"23","author":[{"given":"Junshuai","family":"Yan","sequence":"first","affiliation":[{"name":"School of New Energy, North China Electric Power University, Beijing 102206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of New Energy, North China Electric Power University, Beijing 102206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"School of New Energy, North China Electric Power University, Beijing 102206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Ren","sequence":"additional","affiliation":[{"name":"School of New Energy, North China Electric Power University, Beijing 102206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, G., Wang, C., Zhang, D., and Yang, G. (2021). An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring. Sensors, 21.","DOI":"10.3390\/s21165654"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/j.renene.2021.09.008","article-title":"Wind Turbine Blade Icing Diagnosis Using Hybrid Features and Stacked-XGBoost Algorithm","volume":"180","author":"Tao","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Oliveira-Filho, A., Zemouri, R., Cambron, P., and Tahan, A. (2023). Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model. Energies, 16.","DOI":"10.3390\/en16124544"},{"key":"ref_4","unstructured":"(2023, June 20). Global Wind Report 2023\u2014Global Wind Energy Council. Available online: https:\/\/gwec.net\/global-wind-report-2023."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dhiman, H.S., Deb, D., Carroll, J., Muresan, V., and Unguresan, M.-L. (2020). Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis. Sensors, 20.","DOI":"10.3390\/s20236742"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Feng, Y., Zhang, X., Jiang, H., and Li, J. (2022). Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition. Sensors, 22.","DOI":"10.3390\/s22208017"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, S., Yang, P., Yu, H., Bai, J., Feng, W., Su, Y., and Si, Y. (2022). A 2DCNN-RF Model for Offshore Wind Turbine High-Speed Bearing-Fault Diagnosis under Noisy Environment. Energies, 15.","DOI":"10.3390\/en15093340"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"114469","DOI":"10.1016\/j.apenergy.2019.114469","article-title":"Wind Turbine Fault Detection Based on Expanded Linguistic Terms and Rules Using Non-Singleton Fuzzy Logic","volume":"262","author":"Qu","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.renene.2020.12.116","article-title":"Fault Detection and Diagnosis of a Blade Pitch System in a Floating Wind Turbine Based on Kalman Filters and Artificial Neural Networks","volume":"169","author":"Cho","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1109\/TIE.2006.878304","article-title":"Application of Fully Decoupled Parity Equation in Fault Detection and Identification of DC Motors","volume":"53","author":"Chan","year":"2006","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"117698","DOI":"10.1016\/j.eswa.2022.117698","article-title":"Fault Diagnosis in Wind Turbines Based on ANFIS and Takagi\u2013Sugeno Interval Observers","volume":"206","author":"Puig","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Borja-Jaimes, V., Adam-Medina, M., L\u00f3pez-Zapata, B.Y., Vela Vald\u00e9s, L.G., Claudio Pachecano, L., and S\u00e1nchez Coronado, E.M. (2021). Sliding Mode Observer-Based Fault Detection and Isolation Approach for a Wind Turbine Benchmark. Processes, 10.","DOI":"10.3390\/pr10010054"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Meng, L., Su, Y., Kong, X., Lan, X., Li, Y., Xu, T., and Ma, J. (2022). A Probabilistic Bayesian Parallel Deep Learning Framework for Wind Turbine Bearing Fault Diagnosis. Sensors, 22.","DOI":"10.3390\/s22197644"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Afridi, Y.S., Hasan, L., Ullah, R., Ahmad, Z., and Kim, J.-M. (2023). LSTM-Based Condition Monitoring and Fault Prognostics of Rolling Element Bearings Using Raw Vibrational Data. Machines, 11.","DOI":"10.3390\/machines11050531"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1016\/j.renene.2020.12.111","article-title":"A Novel Wind Turbine Health Condition Monitoring Method Based on Composite Variational Mode Entropy and Weighted Distribution Adaptation","volume":"168","author":"Ren","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Knap, P., Lalik, K., and Ba\u0142azy, P. (2023). Boosted Convolutional Neural Network Algorithm for the Classification of the Bearing Fault Form 1-D Raw Sensor Data. Sensors, 23.","DOI":"10.3390\/s23094295"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109347","DOI":"10.1016\/j.measurement.2021.109347","article-title":"A New Hybrid Model Based on Secondary Decomposition, Reinforcement Learning and SRU Network for Wind Turbine Gearbox Oil Temperature Forecasting","volume":"178","author":"Liu","year":"2021","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.renene.2020.04.096","article-title":"Acoustical Damage Detection of Wind Turbine Blade Using the Improved Incremental Support Vector Data Description","volume":"156","author":"Chen","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ding, S., Yang, C., and Zhang, S. (2023). Acoustic-Signal-Based Damage Detection of Wind Turbine Blades\u2014A Review. Sensors, 23.","DOI":"10.3390\/s23114987"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1109\/TSTE.2017.2690835","article-title":"Current-Based Gear Fault Detection for Wind Turbine Gearboxes","volume":"8","author":"Lu","year":"2017","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1016\/j.apenergy.2015.12.049","article-title":"Analyzing Wind Turbine Directional Behavior: SCADA Data Mining Techniques for Efficiency and Power Assessment","volume":"185","author":"Castellani","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xiao, X., Liu, J., Liu, D., Tang, Y., and Zhang, F. (2022). Condition Monitoring of Wind Turbine Main Bearing Based on Multivariate Time Series Forecasting. Energies, 15.","DOI":"10.3390\/en15051951"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Astolfi, D. (2023). Wind Turbine Drivetrain Condition Monitoring through SCADA-Collected Temperature Data: Discussion of Selected Recent Papers. Energies, 16.","DOI":"10.3390\/en16093614"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Astolfi, D., De Caro, F., and Vaccaro, A. (2023). Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques. Sensors, 23.","DOI":"10.3390\/s23125376"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.1109\/TEC.2021.3075897","article-title":"Wind Turbine Gearbox Anomaly Detection Based on Adaptive Threshold and Twin Support Vector Machines","volume":"36","author":"Dhiman","year":"2021","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/j.renene.2021.07.085","article-title":"Fault Detection by an Ensemble Framework of Extreme Gradient Boosting (XGBoost) in the Operation of Offshore Wind Turbines","volume":"179","author":"Trizoglou","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.apenergy.2016.01.133","article-title":"A Generalized Model for Wind Turbine Anomaly Identification Based on SCADA Data","volume":"168","author":"Sun","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1049\/iet-rpg.2018.0156","article-title":"SCADA Based Wind Turbine Anomaly Detection Using Gaussian Process (GP) Models for Wind Turbine Condition Monitoring Purposes","volume":"12","author":"Pandit","year":"2018","journal-title":"IET Renew. Power Gener."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"117342","DOI":"10.1016\/j.apenergy.2021.117342","article-title":"Multi-Target Normal Behaviour Models for Wind Farm Condition Monitoring","volume":"300","author":"Meyer","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.renene.2018.04.059","article-title":"An Unsupervised Spatiotemporal Graphical Modeling Approach for Wind Turbine Condition Monitoring","volume":"127","author":"Yang","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.renene.2020.04.148","article-title":"System-Wide Anomaly Detection in Wind Turbines Using Deep Autoencoders","volume":"157","author":"Bangalore","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1016\/j.renene.2019.09.041","article-title":"Anomaly Detection for Wind Turbines Based on the Reconstruction of Condition Parameters Using Stacked Denoising Autoencoders","volume":"147","author":"Chen","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.apenergy.2019.03.044","article-title":"Short-Term Forecasting and Uncertainty Analysis of Wind Turbine Power Based on Long Short-Term Memory Network and Gaussian Mixture Model","volume":"241","author":"Zhang","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.renene.2018.10.031","article-title":"Fault Diagnosis of Wind Turbine Based on Long Short-Term Memory Networks","volume":"133","author":"Lei","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1016\/j.renene.2021.03.078","article-title":"Anomaly Detection and Critical SCADA Parameters Identification for Wind Turbines Based on LSTM-AE Neural Network","volume":"172","author":"Chen","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1016\/j.renene.2019.07.033","article-title":"Condition Monitoring of Wind Turbines Based on Spatio-Temporal Fusion of SCADA Data by Convolutional Neural Networks and Gated Recurrent Units","volume":"146","author":"Kong","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_37","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109094","DOI":"10.1016\/j.measurement.2021.109094","article-title":"Fault Detection of Wind Turbine Based on SCADA Data Analysis Using CNN and LSTM with Attention Mechanism","volume":"175","author":"Xiang","year":"2021","journal-title":"Measurement"},{"key":"ref_39","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle Swarm Optimization. Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, Australia."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.knosys.2014.05.004","article-title":"Crisscross Optimization Algorithm and Its Application","volume":"67","author":"Meng","year":"2014","journal-title":"Knowl. Based Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","article-title":"A Novel Swarm Intelligence Optimization Approach: Sparrow Search Algorithm","volume":"8","author":"Xue","year":"2020","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Nadimi-Shahraki, M.H., Zamani, H., Fatahi, A., and Mirjalili, S. (2023). MFO-SFR: An Enhanced Moth-Flame Optimization Algorithm Using an Effective Stagnation Finding and Replacing Strategy. Mathematics, 11.","DOI":"10.3390\/math11040862"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1214\/14-AOS1245","article-title":"Wild Binary Segmentation for Multiple Change-Point Detection","volume":"42","author":"Fryzlewicz","year":"2014","journal-title":"Ann. Stat."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1109\/TSP.2021.3087031","article-title":"Change Point Detection in Time Series Data Using Autoencoders with a Time-Invariant Representation","volume":"69","author":"Bertrand","year":"2021","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107299","DOI":"10.1016\/j.sigpro.2019.107299","article-title":"Selective Review of Offline Change Point Detection Methods","volume":"167","author":"Truong","year":"2020","journal-title":"Signal Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1093\/biomet\/41.1-2.100","article-title":"Continuous Inspection Schemes","volume":"41","author":"Page","year":"1954","journal-title":"Biometrika"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5873\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:00:10Z","timestamp":1760126410000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5873"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,25]]},"references-count":46,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23135873"],"URL":"https:\/\/doi.org\/10.3390\/s23135873","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,25]]}}}