{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:52:15Z","timestamp":1770292335607,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61973180"],"award-info":[{"award-number":["61973180"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6217072142"],"award-info":[{"award-number":["6217072142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020-19"],"award-info":[{"award-number":["2020-19"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Production-Education Integration Postgraduate Joint Training Demonstration Base Project","award":["61973180"],"award-info":[{"award-number":["61973180"]}]},{"name":"Shandong Provincial Production-Education Integration Postgraduate Joint Training Demonstration Base Project","award":["6217072142"],"award-info":[{"award-number":["6217072142"]}]},{"name":"Shandong Provincial Production-Education Integration Postgraduate Joint Training Demonstration Base Project","award":["2020-19"],"award-info":[{"award-number":["2020-19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.<\/jats:p>","DOI":"10.3390\/s23156741","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T02:10:45Z","timestamp":1690510245000},"page":"6741","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA"],"prefix":"10.3390","volume":"23","author":[{"given":"Lingchao","family":"Kong","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongtao","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guozhu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2447","DOI":"10.1038\/s41467-023-37536-3","article-title":"Grid integration feasibility and investment planning of offshore wind power under carbon-neutral transition in China","volume":"14","author":"Guo","year":"2023","journal-title":"J. Nat. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111847","DOI":"10.1016\/j.rser.2021.111847","article-title":"The complex end-of-life of wind turbine blades: A review of the European context","volume":"155","author":"Beauson","year":"2022","journal-title":"J. Renew. Sustain. Energy Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kale, A.P., Wahul, R.M., Patange, A.D., Soman, R., and Ostachowicz, W. (2023). Development of Deep Belief Network for Tool Faults Recognition. Sensors, 23.","DOI":"10.3390\/s23041872"},{"key":"ref_4","first-page":"127","article-title":"Application of machine learning for tool condition monitoring in turning","volume":"56","author":"Patange","year":"2022","journal-title":"Sound Vib."},{"key":"ref_5","first-page":"200196","article-title":"Application of metaheuristic optimization based support vector machine for milling cutter health monitoring","volume":"18","author":"Bajaj","year":"2023","journal-title":"Intell. Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Santolamazza, A., Dadi, D., and Introna, V. (2021). A data-mining approach for wind turbine fault detection based on SCADA data analysis using artificial neural networks. Energies, 14.","DOI":"10.3390\/en14071845"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"119373","DOI":"10.1016\/j.apenergy.2022.119373","article-title":"A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification","volume":"321","author":"Wang","year":"2022","journal-title":"J. Appl. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jawad, R.S., and Abid, H. (2022). Fault Detection in HVDC System with Gray Wolf Optimization Algorithm Based on Artificial Neural Network. Energies, 15.","DOI":"10.3390\/en15207775"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.engappai.2003.09.006","article-title":"Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection","volume":"16","author":"Samanta","year":"2003","journal-title":"J. Eng. Appl. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104546","DOI":"10.1016\/j.conengprac.2020.104546","article-title":"SCADA-data-based wind turbine fault detection: A dynamic model sensor method","volume":"102","author":"Zhang","year":"2020","journal-title":"J. Control Eng. Pract."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"110961","DOI":"10.1016\/j.rser.2021.110961","article-title":"Critical comparison of power-based wind turbine fault-detection methods using a realistic framework for SCADA data simulation","volume":"144","author":"Aziz","year":"2021","journal-title":"J. Renew. Sustain. Energy Rev."},{"key":"ref_12","first-page":"61","article-title":"Wind turbine bearing fault diagnosis method based on an improved denoising AutoEncoder","volume":"50","author":"Song","year":"2022","journal-title":"J. Power Syst. Prot. Control"},{"key":"ref_13","first-page":"2348","article-title":"A Wind Turbine Fault Diagnosis Method Based on Siamese Deep Neural Network","volume":"34","author":"Liu","year":"2022","journal-title":"J. Syst. Simul."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"119102","DOI":"10.1016\/j.eswa.2022.119102","article-title":"Wind turbine fault detection based on deep residual networks","volume":"213","author":"Liu","year":"2023","journal-title":"J. Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tang, M., Cao, C., Wu, H., Zhu, H., Tang, J., Peng, Z., and Wang, Y. (2022). Fault Detection of Wind Turbine Gearboxes Based on IBOA-ERF. Sensors, 22.","DOI":"10.3390\/s22186826"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10175","DOI":"10.1016\/j.amc.2011.05.013","article-title":"Generalized particle swarm optimization algorithm-Theoretical and empirical analysis with application in fault detection","volume":"217","year":"2011","journal-title":"J. Appl. Math. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lee, D.H., Ahn, J.H., and Koh, B.H. (2017). Fault detection of bearing systems through EEMD and optimization algorithm. Sensors, 17.","DOI":"10.3390\/s17112477"},{"key":"ref_18","first-page":"213","article-title":"Fault Warning of Power Plant Fans based on Long Short-term Memory Neural Network and Bayesian Optimization","volume":"37","author":"Lei","year":"2022","journal-title":"J. Eng. Therm. Energy Power"},{"key":"ref_19","unstructured":"Zhang, Y.F., Pi, Z.Y., Zhu, R.Q., Song, J.X., and Shi, J.J. (2022). Wind Power Prediction Based on WOA-BiLSTM Neural NetworkElectric. J. Eng., 28\u201331."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, L., Wang, Y., Guo, Y., and Hu, G. (2022). An improved reptile search algorithm based on l\u00e9vy flight and interactive crossover strategy to engineering application. Mathematics, 10.","DOI":"10.3390\/math10132329"},{"key":"ref_21","first-page":"6639","article-title":"CatBoost: Unbiased boosting with categorical features","volume":"31","author":"Prokhorenkova","year":"2018","journal-title":"J. Adv. Neural Inf. Process. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.1016\/j.egyr.2021.03.017","article-title":"Predictive model of cooling load for ice storage air-conditioning system by using GBDT","volume":"7","author":"Zhang","year":"2021","journal-title":"J. Energy Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1016\/j.asej.2020.11.011","article-title":"Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia","volume":"12","author":"Osman","year":"2021","journal-title":"J. Ain Shams Eng. J."},{"key":"ref_24","first-page":"3149","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"J. Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Patange, A.D., Pardeshi, S.S., Jegadeeshwaran, R., Zarkar, A., and Verma, K. (2022). Augmentation of Decision Tree Model through Hyper-Parameters Tuning for Monitoring of Cutting Tool Faults Based on Vibration Signatures. J. Vib. Eng. Technol., 1\u201319.","DOI":"10.1007\/s42417-022-00781-9"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00369-8","article-title":"CatBoost for big data: An interdisciplinary review","volume":"7","author":"Hancock","year":"2020","journal-title":"J. Big Data"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., and Girshick, R. (2017). Focal loss for dense object detection. Proc. IEEE Int. Conf. Comput. Vis., 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"116158","DOI":"10.1016\/j.eswa.2021.116158","article-title":"Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer","volume":"191","author":"Abualigah","year":"2022","journal-title":"J. Expert Syst. Appl."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"J. Adv. Eng. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"113338","DOI":"10.1016\/j.eswa.2020.113338","article-title":"Chimp optimization algorithm","volume":"149","author":"Khishe","year":"2020","journal-title":"J. Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"20263","DOI":"10.1007\/s00521-022-07575-w","article-title":"Development of L\u00e9vy flight-based reptile search algorithm with local search ability for power systems engineering design problems","volume":"34","author":"Ekinci","year":"2022","journal-title":"J. Neural Comput. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17257","DOI":"10.1007\/s00521-022-07369-0","article-title":"Improved reptile search algorithm with novel mean transition mechanism for constrained industrial engineering problems","volume":"34","author":"Almotairi","year":"2022","journal-title":"J. Neural Comput. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6741\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:21:07Z","timestamp":1760127667000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6741"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,28]]},"references-count":33,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23156741"],"URL":"https:\/\/doi.org\/10.3390\/s23156741","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,28]]}}}