{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T04:09:47Z","timestamp":1748578187811,"version":"3.41.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation","doi-asserted-by":"crossref","award":["12362005"],"award-info":[{"award-number":["12362005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Project of Natural Science Foundation of Ningxia","award":["2024AAC02003"],"award-info":[{"award-number":["2024AAC02003"]}]},{"name":"Ningxia higher education first-class discipline construction funding project","award":["NXYLXK2017B09"],"award-info":[{"award-number":["NXYLXK2017B09"]}]},{"name":"Postgraduate innovation project of North Minzu University","award":["YCX24282"],"award-info":[{"award-number":["YCX24282"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07438-w","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T19:22:55Z","timestamp":1748546575000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The hybrid prediction of wind turbine gearbox oil temperature based on chaotic theory"],"prefix":"10.1007","volume":"81","author":[{"given":"Cheng","family":"Huang","sequence":"first","affiliation":[]},{"given":"Shaojuan","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Changlin","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xinyi","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"issue":"1","key":"7438_CR1","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.ress.2010.07.007","volume":"96","author":"JJ Nielsen","year":"2011","unstructured":"Nielsen JJ, S\u00f8rensen JD (2011) On risk-based operation and maintenance of offshore wind turbine components. Reliab Eng Syst Saf 96(1):218\u2013229. https:\/\/doi.org\/10.1016\/j.ress.2010.07.007","journal-title":"Reliab Eng Syst Saf"},{"issue":"9","key":"7438_CR2","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3901\/JME.2022.09.119","volume":"58","author":"D Xiang","year":"2022","unstructured":"Xiang D, Wei YZ, Shen YH, Sun XY (2022) Research on thermal network modeling and temperature calculation method for wind turbine gearbox lubrication oil temperature overrun fault. J Mech Eng 58(9):119\u2013135","journal-title":"J Mech Eng"},{"issue":"3","key":"7438_CR3","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.3390\/en16031125","volume":"16","author":"F Zhang","year":"2023","unstructured":"Zhang F, Chen M, Zhu Y et al (2023) A review of fault diagnosis, status prediction, and evaluation technology for wind turbines. Energies 16(3):1125. https:\/\/doi.org\/10.3390\/en16031125","journal-title":"Energies"},{"issue":"3","key":"7438_CR4","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1002\/we.2290","volume":"22","author":"J Carroll","year":"2019","unstructured":"Carroll J, Koukoura S, McDonald A et al (2019) Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques. Wind Energy 22(3):360\u2013375. https:\/\/doi.org\/10.1002\/we.2290","journal-title":"Wind Energy"},{"issue":"3","key":"7438_CR5","doi-asserted-by":"publisher","first-page":"175","DOI":"10.3390\/machines12030175","volume":"12","author":"YF Cui","year":"2024","unstructured":"Cui YF, Zhang YH, He WD et al (2024) Temperature prediction for 3 MW wind-turbine gearbox based on thermal network model. Machines 12(3):175. https:\/\/doi.org\/10.3390\/machines12030175","journal-title":"Machines"},{"issue":"02\u201311\u201303\u20130013","key":"7438_CR6","doi-asserted-by":"publisher","first-page":"163","DOI":"10.4271\/02-11-03-0013","volume":"11","author":"S Deshpande","year":"2018","unstructured":"Deshpande S, Joshi H, Madhavan J et al (2018) Two-way coupled CFD approach for predicting gear temperature of oil jet lubricated transmissions. SAE Int J Commer Veh 11(02\u201311\u201303\u20130013):163\u2013170. https:\/\/doi.org\/10.4271\/02-11-03-0013","journal-title":"SAE Int J Commer Veh"},{"key":"7438_CR7","doi-asserted-by":"publisher","unstructured":"Yang Y, Bai Y, Li C, et al (2018) Application research of ARIMA model in wind turbine gearbox fault trend prediction. In: 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), pp 520\u2013526. https:\/\/doi.org\/10.1109\/SDPC.2018.8664793","DOI":"10.1109\/SDPC.2018.8664793"},{"key":"7438_CR8","doi-asserted-by":"publisher","unstructured":"Liang T, Yang G, Dong Y, et al (2018) Predicting temperatures of wind turbine gearbox by a variable-weight combined model. In: 2018 24th International Conference on Automation and Computing (ICAC), pp 1\u20136. https:\/\/doi.org\/10.23919\/IConAC.2018.8749036","DOI":"10.23919\/IConAC.2018.8749036"},{"issue":"2","key":"7438_CR9","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/j.ymssp.2009.08.004","volume":"24","author":"HT Pham","year":"2010","unstructured":"Pham HT, Yang BS (2010) Estimation and forecasting of machine health condition using ARMA\/GARCH model. Mech Syst Signal Process 24(2):546\u2013558. https:\/\/doi.org\/10.1016\/j.ymssp.2009.08.004","journal-title":"Mech Syst Signal Process"},{"key":"7438_CR10","doi-asserted-by":"publisher","first-page":"185557","DOI":"10.1109\/ACCESS.2020.3029435","volume":"8","author":"Y Liu","year":"2020","unstructured":"Liu Y, Wu Z, Wang X (2020) Research on fault diagnosis of wind turbine based on SCADA data. IEEE Access 8:185557\u2013185569. https:\/\/doi.org\/10.1109\/ACCESS.2020.3029435","journal-title":"IEEE Access"},{"issue":"6","key":"7438_CR11","first-page":"539","volume":"58","author":"H Zhongshan","year":"2018","unstructured":"Zhongshan H, Ling T, Dong X et al (2018) Prediction of oil temperature variations in a wind turbine gearbox based on PCA and an SPC-dynamic neural network hybrid. J Tsinghua Univ Sci Technol 58(6):539\u2013546","journal-title":"J Tsinghua Univ Sci Technol"},{"issue":"08","key":"7438_CR12","first-page":"948","volume":"29","author":"W Hongbin","year":"2018","unstructured":"Hongbin W, Hong W, Qun H (2018) Condition monitoring method for wind turbine main bearings based on DBN. China Mech Eng 29(08):948\u2013953","journal-title":"China Mech Eng"},{"key":"7438_CR13","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1016\/j.renene.2021.09.070","volume":"181","author":"Y Zhu","year":"2022","unstructured":"Zhu Y, Zhu C, Tan J et al (2022) Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis. Renew Energy 181:1167\u20131176. https:\/\/doi.org\/10.1016\/j.renene.2021.09.070","journal-title":"Renew Energy"},{"key":"7438_CR14","doi-asserted-by":"crossref","unstructured":"Guo R, Zhang G, Zhang Q, et al (2021) Early fault detection of wind turbine gearbox based on Adam-trained LSTM. In: 2021 6th International Conference on Power and Renewable Energy (ICPRE), pp 285\u2013289","DOI":"10.1109\/ICPRE52634.2021.9635550"},{"issue":"20","key":"7438_CR15","doi-asserted-by":"publisher","first-page":"3920","DOI":"10.3390\/en12203920","volume":"12","author":"Q Zhao","year":"2019","unstructured":"Zhao Q, Bao K, Wang J et al (2019) An online hybrid model for temperature prediction of wind turbine gearbox components. Energies 12(20):3920. https:\/\/doi.org\/10.3390\/en12203920","journal-title":"Energies"},{"key":"7438_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118128","volume":"208","author":"C Wang","year":"2022","unstructured":"Wang C, Chen Y, Zhang S et al (2022) Stock market index prediction using deep Transformer model. Expert Syst Appl 208:118128. https:\/\/doi.org\/10.1016\/j.eswa.2022.118128","journal-title":"Expert Syst Appl"},{"key":"7438_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.129753","volume":"288","author":"S Wang","year":"2024","unstructured":"Wang S, Shi J, Yang W et al (2024) High and low frequency wind power prediction based on Transformer and BiGRU-Attention. Energy 288:129753. https:\/\/doi.org\/10.1016\/j.energy.2023.129753","journal-title":"Energy"},{"key":"7438_CR18","unstructured":"Chen J, Dai H, Wang S, et al (2024) Improving accuracy and efficiency in time series forecasting with an optimized transformer model. Eng Lett 32(1)"},{"key":"7438_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2023.119671","volume":"220","author":"G Xiu-Yan","year":"2024","unstructured":"Xiu-Yan G, Jie-Mei L, Yuan Y et al (2024) Global horizontal irradiance prediction model considering the effect of aerosol optical depth based on the Informer model. Renew Energy 220:119671. https:\/\/doi.org\/10.1016\/j.renene.2023.119671","journal-title":"Renew Energy"},{"key":"7438_CR20","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.apm.2019.04.032","volume":"73","author":"H Liu","year":"2019","unstructured":"Liu H, Xu Y, Chen C (2019) Improved pollution forecasting hybrid algorithms based on the ensemble method. Appl Math Model 73:473\u2013486. https:\/\/doi.org\/10.1016\/j.apm.2019.04.032","journal-title":"Appl Math Model"},{"issue":"3","key":"7438_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1670679.1670680","volume":"42","author":"F Salfner","year":"2010","unstructured":"Salfner F, Lenk M, Malek M (2010) A survey of online failure prediction methods. ACM Comput Surv (CSUR) 42(3):1\u201342. https:\/\/doi.org\/10.1145\/1670679.1670680","journal-title":"ACM Comput Surv (CSUR)"},{"key":"7438_CR22","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.renene.2019.07.033","volume":"146","author":"Z Kong","year":"2020","unstructured":"Kong Z, Tang B, Deng L et al (2020) Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units. Renew Energy 146:760\u2013768. https:\/\/doi.org\/10.1016\/j.renene.2019.07.033","journal-title":"Renew Energy"},{"issue":"17","key":"7438_CR23","doi-asserted-by":"publisher","first-page":"6275","DOI":"10.3390\/en16176275","volume":"16","author":"Z Fu","year":"2023","unstructured":"Fu Z, Zhou Z, Zhu J et al (2023) Condition monitoring method for the gearboxes of offshore wind turbines based on oil temperature prediction. Energies 16(17):6275. https:\/\/doi.org\/10.3390\/en16176275","journal-title":"Energies"},{"issue":"9","key":"7438_CR24","doi-asserted-by":"publisher","first-page":"24506","DOI":"10.1007\/s11356-022-23893-x","volume":"30","author":"H Wang","year":"2023","unstructured":"Wang H, Zhao X, Wang W (2023) Fault diagnosis and prediction of wind turbine gearbox based on a new hybrid model. Environ Sci Pollut Res 30(9):24506\u201324520. https:\/\/doi.org\/10.1007\/s11356-022-23893-x","journal-title":"Environ Sci Pollut Res"},{"issue":"11","key":"7438_CR25","doi-asserted-by":"publisher","first-page":"248","DOI":"10.3390\/machines9110248","volume":"9","author":"G Yan","year":"2021","unstructured":"Yan G, Yu C, Bai Y (2021) Wind turbine bearing temperature forecasting using a new data-driven ensemble approach. Machines 9(11):248. https:\/\/doi.org\/10.3390\/machines9110248","journal-title":"Machines"},{"issue":"20","key":"7438_CR26","doi-asserted-by":"publisher","first-page":"3920","DOI":"10.3390\/en12203920","volume":"12","author":"Q Zhao","year":"2019","unstructured":"Zhao Q, Bao K, Wang J et al (2019) An online hybrid model for temperature prediction of wind turbine gearbox components. Energies 12(20):3920. https:\/\/doi.org\/10.3390\/en12203920","journal-title":"Energies"},{"key":"7438_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109347","volume":"178","author":"H Liu","year":"2021","unstructured":"Liu H, Yu C, Yu C (2021) A new hybrid model based on secondary decomposition, reinforcement learning and SRU network for wind turbine gearbox oil temperature forecasting. Measurement 178:109347. https:\/\/doi.org\/10.1016\/j.measurement.2021.109347","journal-title":"Measurement"},{"issue":"6","key":"7438_CR28","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s13201-023-01943-0","volume":"13","author":"F Ahmadi","year":"2023","unstructured":"Ahmadi F, Tohidi M, Sadrianzade M (2023) Streamflow prediction using a hybrid methodology based on variational mode decomposition (VMD) and machine learning approaches. Appl Water Sci 13(6):135. https:\/\/doi.org\/10.1007\/s13201-023-01943-0","journal-title":"Appl Water Sci"},{"key":"7438_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.132766","volume":"307","author":"S Cui","year":"2024","unstructured":"Cui S, Lyu S, Ma Y et al (2024) Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE. Energy 307:132766. https:\/\/doi.org\/10.1016\/j.energy.2024.132766","journal-title":"Energy"},{"key":"7438_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2024.116477","author":"X Duan","year":"2024","unstructured":"Duan X (2024) Settlement prediction of Nanjing Metro Line 10 with HOA-VMD-LSTM. Measurement. https:\/\/doi.org\/10.1016\/j.measurement.2024.116477","journal-title":"Measurement"},{"key":"7438_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2024.119300","volume":"312","author":"Y Huang","year":"2024","unstructured":"Huang Y, Li G (2024) Research on tidal energy prediction method based on improved time-varying filter-empirical mode decomposition and confluent double-stream neural network. Ocean Eng 312:119300. https:\/\/doi.org\/10.1016\/j.oceaneng.2024.119300","journal-title":"Ocean Eng"},{"key":"7438_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s11071-024-10474-5","author":"S Mo","year":"2024","unstructured":"Mo S, Hu Q, Zhao X et al (2024) Research on nonlinear dynamics of planetary gear system of wind turbine gearbox considering wear. Nonlinear Dyn. https:\/\/doi.org\/10.1007\/s11071-024-10474-5","journal-title":"Nonlinear Dyn"},{"key":"7438_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.124167","volume":"253","author":"W Sun","year":"2022","unstructured":"Sun W, Zhang J (2022) A novel carbon price prediction model based on optimized least square support vector machine combining characteristic-scale decomposition and phase space reconstruction. Energy 253:124167. https:\/\/doi.org\/10.1016\/j.energy.2022.124167","journal-title":"Energy"},{"issue":"3","key":"7438_CR34","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2013","unstructured":"Dragomiretskiy K, Zosso D (2013) Variational mode decomposition. IEEE Trans Signal Process 62(3):531\u2013544. https:\/\/doi.org\/10.1109\/TSP.2013.2288675","journal-title":"IEEE Trans Signal Process"},{"key":"7438_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121744","volume":"238","author":"S Zhao","year":"2024","unstructured":"Zhao S, Zhang T, Cai L et al (2024) Triangulation topology aggregation optimizer: a novel mathematics-based meta-heuristic algorithm for continuous optimization and engineering applications. Expert Syst Appl 238:121744. https:\/\/doi.org\/10.1016\/j.eswa.2023.121744","journal-title":"Expert Syst Appl"},{"key":"7438_CR36","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1406.1078","author":"K Cho","year":"2014","unstructured":"Cho K, Merri\u00ebnboer B, Gulcehre C et al (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. Comput Lang. https:\/\/doi.org\/10.48550\/arXiv.1406.1078","journal-title":"Comput Lang"},{"issue":"12","key":"7438_CR37","doi-asserted-by":"publisher","first-page":"11106","DOI":"10.1609\/aaai.v35i12.17325","volume":"35","author":"H Zhou","year":"2021","unstructured":"Zhou H, Zhang S, Peng J et al (2021) Informer: Beyond efficient transformer for long sequence time-series forecasting. Proc AAAI Conf Artif Intell 35(12):11106\u201311115. https:\/\/doi.org\/10.1609\/aaai.v35i12.17325","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"1","key":"7438_CR38","doi-asserted-by":"publisher","first-page":"2133","DOI":"10.1038\/s41598-024-52261-7","volume":"14","author":"B Tu","year":"2024","unstructured":"Tu B, Bai K, Zhan C et al (2024) Real-time prediction of ROP based on GRU-Informer. Sci Rep 14(1):2133. https:\/\/doi.org\/10.1038\/s41598-024-52261-7","journal-title":"Sci Rep"},{"issue":"1","key":"7438_CR39","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.medengphy.2008.04.005","volume":"31","author":"W Chen","year":"2009","unstructured":"Chen W, Zhuang J, Yu W et al (2009) Measuring complexity using fuzzyen, apen, and sampen. Med Eng Phys 31(1):61\u201368. https:\/\/doi.org\/10.1016\/j.medengphy.2008.04.005","journal-title":"Med Eng Phys"},{"key":"7438_CR40","doi-asserted-by":"publisher","unstructured":"Tang L, Liang J (2011) CC method to phase space reconstruction based on multivariate time series. In: 2011 2nd International Conference on Intelligent Control and Information Processing, vol 1, pp 438\u2013441. https:\/\/doi.org\/10.1109\/ICICIP.2011.6008282","DOI":"10.1109\/ICICIP.2011.6008282"},{"issue":"1\u20132","key":"7438_CR41","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/0167-2789(94)90226-7","volume":"73","author":"MT Rosenstein","year":"1994","unstructured":"Rosenstein MT, Collins JJ, De Luca CJ (1994) Reconstruction expansion as a geometry-based framework for choosing proper delay times. Physica D 73(1\u20132):82\u201398. https:\/\/doi.org\/10.1016\/0167-2789(94)90226-7","journal-title":"Physica D"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07438-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07438-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07438-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T19:22:59Z","timestamp":1748546579000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07438-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,29]]},"references-count":41,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["7438"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07438-w","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,29]]},"assertion":[{"value":"11 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"927"}}