{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T06:47:34Z","timestamp":1782542854991,"version":"3.54.5"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s00521-025-11777-3","type":"journal-article","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T06:15:44Z","timestamp":1782540944000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-time early warning of multi-measurement point time series of hydropower generating units based on an integrated machine learning model"],"prefix":"10.1007","volume":"38","author":[{"given":"Dong","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenfeng","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxiang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"11777_CR1","volume":"49","author":"K Kumar","year":"2022","unstructured":"Kumar K, Saini RP (2022) A review on operation and maintenance of hydropower plants. Sustain Energy Technol Assess 49:101704","journal-title":"Sustain Energy Technol Assess"},{"key":"11777_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107238","volume":"205","author":"M Bulut","year":"2021","unstructured":"Bulut M, Ozcan E (2021) A new approach to determine maintenance periods of the most critical hydroelectric power plant equipment. Reliab Eng Syst Saf 205:107238","journal-title":"Reliab Eng Syst Saf"},{"key":"11777_CR3","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1016\/j.renene.2020.08.017","volume":"162","author":"H Rauf","year":"2020","unstructured":"Rauf H, Gull MS, Arshad N (2020) Complementing hydroelectric power with floating solar PV for daytime peak electricity demand. Renew Energy 162:1227\u20131242","journal-title":"Renew Energy"},{"key":"11777_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2021.112332","volume":"155","author":"Z Xiaosan","year":"2021","unstructured":"Xiaosan Z, Qingquan J, Iqbal KS et al (2021) Achieving sustainability and energy efficiency goals: assessing the impact of hydroelectric and renewable electricity generation on carbon dioxide emission in China. Energy Policy 155:112332","journal-title":"Energy Policy"},{"issue":"01","key":"11777_CR5","first-page":"33","volume":"38","author":"Z Li","year":"2023","unstructured":"Li Z, Wang S, Tang F (2023) IoT-based cloud monitoring system and remote diagnosis for condensing units. Autom Instrum 38(01):33\u201337","journal-title":"Autom Instrum"},{"issue":"4","key":"11777_CR6","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1108\/JMTM-02-2022-0092","volume":"34","author":"B Caiazzo","year":"2023","unstructured":"Caiazzo B, Murino T, Petrillo A et al (2023) An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design, architecture, and experimental validation. J Manuf Technol Manag 34(4):507\u2013534","journal-title":"J Manuf Technol Manag"},{"key":"11777_CR7","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.procir.2021.03.009","volume":"99","author":"A Simeone","year":"2021","unstructured":"Simeone A, Caggiano A, Boun L et al (2021) Cloud-based platform for intelligent healthcare monitoring and risk prevention in hazardous manufacturing contexts. Proc CIRP 99:50\u201356","journal-title":"Proc CIRP"},{"issue":"6","key":"11777_CR8","first-page":"807","volume":"23","author":"X Jiang","year":"2022","unstructured":"Jiang X (2022) Automatic monitoring system of power equipment based on Internet of Things technology. Int J Emerg Electr Power Syst 23(6):807\u2013818","journal-title":"Int J Emerg Electr Power Syst"},{"issue":"04","key":"11777_CR9","first-page":"102","volume":"7","author":"N Zhou","year":"2021","unstructured":"Zhou N, Xu J, Jiang S et al (2021) Prediction strategy for fluctuation of monitoring parameters of hydropower units based on probability statistics. Hydropower Pumped Storage 7(04):102\u2013106","journal-title":"Hydropower Pumped Storage"},{"issue":"02","key":"11777_CR10","first-page":"41","volume":"44","author":"H Liu","year":"2022","unstructured":"Liu H (2022) Fault statistics and CMS system vibration data evaluation analysis based on wind turbine operation and maintenance data. Electr Drive Autom 44(02):41\u201344","journal-title":"Electr Drive Autom"},{"issue":"10","key":"11777_CR11","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1080\/14786451.2021.1890736","volume":"40","author":"IM Black","year":"2021","unstructured":"Black IM, Richmond M, Kolios A (2021) Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management. Int J Sustain Energy 40(10):923\u2013946","journal-title":"Int J Sustain Energy"},{"key":"11777_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2019.101666","volume":"89","author":"D Upadhyay","year":"2020","unstructured":"Upadhyay D, Sampalli S (2020) SCADA (Supervisory Control and Data Acquisition) systems: vulnerability assessment and security recommendations. Computers Secur 89:101666","journal-title":"Computers Secur"},{"issue":"4","key":"11777_CR13","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1007\/s42417-022-00660-3","volume":"11","author":"F Kong","year":"2023","unstructured":"Kong F, Song C, Zhuo Y (2023) Vibration fault analysis of hydropower units based on extreme learning machine optimized by improved sparrow search algorithm. J Vib Eng Technol 11(4):1609\u20131622","journal-title":"J Vib Eng Technol"},{"issue":"10","key":"11777_CR14","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.3390\/math11102274","volume":"11","author":"Y Tan","year":"2023","unstructured":"Tan Y, Zhan C, Pi Y, Zhang C, Song J, Chen Y et al (2023) A hybrid algorithm based on social engineering and an artificial neural network for fault warning detection in hydraulic turbines. Mathematics 11(10):2274\u20132291","journal-title":"Mathematics"},{"issue":"2","key":"11777_CR15","first-page":"2419","volume":"45","author":"J Huang","year":"2023","unstructured":"Huang J, Guo B, Dian S (2023) Condition monitoring and fault diagnosis of hydropower generator based on LSTM correction model. J Intell Fuzzy Syst 45(2):2419\u20132436","journal-title":"J Intell Fuzzy Syst"},{"issue":"7","key":"11777_CR16","doi-asserted-by":"publisher","first-page":"4013","DOI":"10.3390\/app15074013","volume":"15","author":"F Cassano","year":"2025","unstructured":"Cassano F, Crespino AM, Lazoi M, Specchia G, Spennato A (2025) An EWS-LSTM-based deep learning early warning system for industrial machine fault prediction. Appl Sci 15(7):4013\u20134038","journal-title":"Appl Sci"},{"issue":"15","key":"11777_CR17","doi-asserted-by":"publisher","first-page":"3629","DOI":"10.3390\/en17153629","volume":"17","author":"M Cheng","year":"2024","unstructured":"Cheng M, Zhang Q, Cao Y (2024) An early warning model for turbine intermediate-stage flux failure based on an improved heoa algorithm optimizing DMSE-GRU model. Energies 17(15):3629\u20133644","journal-title":"Energies"},{"issue":"12","key":"11777_CR18","doi-asserted-by":"publisher","first-page":"3715","DOI":"10.14778\/3611540.3611559","volume":"16","author":"C Shen","year":"2023","unstructured":"Shen C, Ouyang Q, Li F et al (2023) Lindorm TSDB: A Cloud-Native Time-Series Database for Large-Scale Monitoring Systems. Proc VLDB Endow 16(12):3715\u20133727","journal-title":"Proc VLDB Endow"},{"issue":"2","key":"11777_CR19","first-page":"1","volume":"1","author":"C Wang","year":"2023","unstructured":"Wang C, Qiao J, Huang X et al (2023) Apache IoTDB: A time series database for IoT applications. Proc ACM Manage Data 1(2):1\u201327","journal-title":"Proc ACM Manage Data"},{"key":"11777_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmr.2020.106772","volume":"317","author":"J Luo","year":"2020","unstructured":"Luo J, Zeng Q, Wu K et al (2020) Fast reconstruction of non-uniform sampling multidimensional NMR spectroscopy via a deep neural network. J Magn Reson 317:106772","journal-title":"J Magn Reson"},{"issue":"9","key":"11777_CR21","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1049\/iet-pel.2019.0831","volume":"13","author":"Y Wei","year":"2020","unstructured":"Wei Y, Luo Q, Mantooth A (2020) Comprehensive comparisons between frequency-domain analysis and time-domain analysis for LLC resonant converter. IET Power Electron 13(9):1735\u20131745","journal-title":"IET Power Electron"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11777-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11777-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11777-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T06:15:45Z","timestamp":1782540945000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11777-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"references-count":21,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["11777"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11777-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]},"assertion":[{"value":"28 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no conflicts of interest regarding the publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"557"}}