{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:31:45Z","timestamp":1773001905698,"version":"3.50.1"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"vor","delay-in-days":26,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52479087"],"award-info":[{"award-number":["52479087"]}],"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":["51839010"],"award-info":[{"award-number":["51839010"]}],"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":["U2443226"],"award-info":[{"award-number":["U2443226"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009103","name":"Education Department of Shaanxi Province","doi-asserted-by":"publisher","award":["22JY047"],"award-info":[{"award-number":["22JY047"]}],"id":[{"id":"10.13039\/501100009103","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>This paper proposes a novel recognition approach for operation states of hydroelectric generating units based on data fusion and visualization analysis. First, the principal component analysis (PCA) is employed to simplify signals from multiple channels into a single fused signal, thereby reducing data computation for multiple\u2010channel signals. To reflect the features of fused signals under different operation states, the Gramian angular field (GAF) method is applied to convert the fused signals into image formats, including Gramian angular differential field (GADF) images and Gramian angular summation field (GASF) images, then a depthwise separable convolution neural network (DSCNN) model is established to achieve the operation state recognition for the unit by GADF and GASF images. Based on the operation data from a Kaplan hydroelectric unit at a hydropower station in Southwest China, an experiment on operation recognition is conducted. The proposed PCA\u2013GAF\u2013DSCNN method achieves an accuracy rate of 95.21% with GADF images and 96.41% with GASF images, which were higher than the results obtained using original signals with the GAF\u2013DSCNN method. The results indicate that the fused signal with PCA demonstrates superior performance in the operation recognition compared to the original signals, and PCA\u2013GAF\u2013DSCNN can be used for hydroelectric units effectively. This approach accurately identifies abnormal states in units, making it suitable for monitoring and fault diagnosis in the daily operations of hydroelectric generating units.<\/jats:p>","DOI":"10.1155\/int\/8850566","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T22:34:53Z","timestamp":1738017293000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Intelligent Recognition for Operation States of Hydroelectric Generating Units Based on Data Fusion and Visualization Analysis"],"prefix":"10.1155","volume":"2025","author":[{"given":"Yongfei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1477-7067","authenticated-orcid":false,"given":"Zhuofei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1249-2300","authenticated-orcid":false,"given":"Pengcheng","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109580"},{"key":"e_1_2_9_2_2","article-title":"Application of Complexity Theory to Hydro-Turbine Maintenance","author":"Estrada-Estrada J. H.","year":"2017","journal-title":"3rd IEEE Workshop on Power Electronics and Power Quality Applications"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.2316\/Journal.203.2016.4.203-6282"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108498"},{"key":"e_1_2_9_5_2","article-title":"Hydrological Uncertainty Analysis Using Monte Carlo Simulations to Determine Power Purchasing Agreements for Small Hydroelectric Powerplants","author":"Omar Alfaica A.","year":"2023","journal-title":"Renewable Energy"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.127350"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-7796(03)00152-4"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2020.12.662"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2014.09.025"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.07.053"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engfailanal.2010.09.039"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egypro.2015.06.050"},{"key":"e_1_2_9_13_2","article-title":"Fault Diagnosis of Complex Hydraulic System Based on Fast Mahalanobis Classification System With High-Dimensional Imbalanced Data","author":"Mao T.","year":"2023","journal-title":"Measurement"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.108903"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11220-023-00448-z"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.01.025"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trac.2021.116355"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2014.08.026"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106974"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107419"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2014.02.006"},{"key":"e_1_2_9_22_2","volume-title":"12th IFAC Workshop on Intelligent Manufacturing Systems IMS 2016","author":"Melani A. H. A.","year":"2016"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.07.034"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110664"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.107667"},{"key":"e_1_2_9_26_2","first-page":"113103132","article-title":"Compound Fault Diagnosis of Gearboxes via Multi-Label Convolutional Neural Network and Wavelet Transform","author":"Liang P.","year":"2019","journal-title":"Computers in Industry"},{"key":"e_1_2_9_27_2","article-title":"Damage Localization for Composite Structure Using Guided Wave Signals With Gramian Angular Field Image Coding and Convolutional Neural Networks","author":"Liao Y.","year":"2023","journal-title":"Composite Structures"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.10.017"},{"key":"e_1_2_9_29_2","first-page":"1","article-title":"Physiological Signals Fusion Oriented to Diagnosis\u2014A Review","author":"Uribe Y. F.","year":"2018","journal-title":"Advances in Computing: 13th Colombian Conference, CCC 2018, Cartagena, Colombia, September 26\u201328, 2018, Proceedings 13"},{"key":"e_1_2_9_30_2","first-page":"2598","article-title":"Decision Level Fusion: An Event Driven Approach","author":"Siddharth R.","year":"2018","journal-title":"2018 26th European Signal Processing Conference (EUSIPCO)"},{"key":"e_1_2_9_31_2","article-title":"A Tutorial on Principal Component Analysis","volume":"51","author":"Jonathon S.","year":"2014","journal-title":"International Journal of Remote Sensing"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.107693"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_9_34_2","first-page":"3939","volume-title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence(IJCAI)","author":"Wang Z. G.","year":"2015"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106100"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.saa.2022.121189"},{"key":"e_1_2_9_37_2","article-title":"Distributed Optical Fiber Sensing Intrusion Pattern Recognition Based on GAF and CNN","volume":"99","author":"Lyu C.","year":"2020","journal-title":"Journal of Lightwave Technology"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106397"},{"key":"e_1_2_9_39_2","article-title":"Damage Localization for Composite Structure Using Guided Wave Signals With Gramian Angular Field Image Coding and Convolutional Neural Networks","author":"Liao Y.","year":"2023","journal-title":"Composite Structures"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.snb.2023.134492"},{"key":"e_1_2_9_41_2","first-page":"1251","article-title":"Deep Learning with Depthwise Separable Convolutions","author":"Xception F. C.","year":"2017","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"e_1_2_9_42_2","article-title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","author":"Howard A. G.","year":"2017","journal-title":"arXiv"},{"key":"e_1_2_9_43_2","first-page":"1314","volume-title":"Searching for MobileNetV3[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Howard A.","year":"2019"},{"key":"e_1_2_9_44_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10066-4"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/8850566","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/int\/8850566","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/8850566","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T18:05:12Z","timestamp":1772993112000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/int\/8850566"}},"subtitle":[],"editor":[{"given":"Alexander","family":"Ho\u0161ovsk\u00fd","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1155\/int\/8850566"],"URL":"https:\/\/doi.org\/10.1155\/int\/8850566","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2023-12-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-06","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8850566"}}