{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:43:50Z","timestamp":1780555430015,"version":"3.54.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,12,12]],"date-time":"2018-12-12T00:00:00Z","timestamp":1544572800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People\u2019s Republic of China","doi-asserted-by":"publisher","award":["2016YFE0121700"],"award-info":[{"award-number":["2016YFE0121700"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Development Fund of Macao SAR","award":["MoST-FDCT Joint funding no: (015\/2015\/AMJ)"],"award-info":[{"award-number":["MoST-FDCT Joint funding no: (015\/2015\/AMJ)"]}]},{"name":"Science and Technology Development Fund of Macao SAR","award":["FDCT\/121\/2016\/A3"],"award-info":[{"award-number":["FDCT\/121\/2016\/A3"]}]},{"DOI":"10.13039\/501100004733","name":"University of Macau","doi-asserted-by":"crossref","award":["MYRG2016-FST"],"award-info":[{"award-number":["MYRG2016-FST"]}],"id":[{"id":"10.13039\/501100004733","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004733","name":"University of Macau","doi-asserted-by":"crossref","award":["MYRG2018-00248-FST"],"award-info":[{"award-number":["MYRG2018-00248-FST"]}],"id":[{"id":"10.13039\/501100004733","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Memetic Comp."],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1007\/s12293-018-0277-2","type":"journal-article","created":{"date-parts":[[2018,12,11]],"date-time":"2018-12-11T21:15:54Z","timestamp":1544562954000},"page":"127-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Novel paralleled extreme learning machine networks for fault diagnosis of wind turbine drivetrain"],"prefix":"10.1007","volume":"11","author":[{"given":"Xian-Bo","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9151-7758","authenticated-orcid":false,"given":"Zhi-Xin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pak Kin","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,12,12]]},"reference":[{"key":"277_CR1","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/TMECH.2017.2787686","volume":"23","author":"X-B Wang","year":"2018","unstructured":"Wang X-B, Yang Z-X, Yan X-A (2018) Novel particle swarm optimization-based variational mode decomposition method for the fault diagnosis of complex rotating machinery. IEEE ASME Trans Mechatron 23:68\u201379","journal-title":"IEEE ASME Trans Mechatron"},{"key":"277_CR2","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.jsv.2014.09.025","volume":"335","author":"Y Yang","year":"2015","unstructured":"Yang Y, Dong X, Peng Z, Zhang W, Meng G (2015) Vibration signal analysis using parameterized timefrequency method for features extraction of varying-speed rotary machinery. J Sound Vib 335:350\u2013366","journal-title":"J Sound Vib"},{"issue":"7","key":"277_CR3","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1109\/TNNLS.2013.2293418","volume":"25","author":"L Shao","year":"2014","unstructured":"Shao L, Liu L, Li X (2014) Feature learning for image classification via multiobjective genetic programming. IEEE Trans Neural Netw Learn Syst 25(7):1359\u20131371","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"277_CR4","doi-asserted-by":"publisher","first-page":"2226","DOI":"10.1109\/TII.2013.2243743","volume":"9","author":"X Dai","year":"2013","unstructured":"Dai X, Gao Z (2013) From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Trans Ind Inf 9(4):2226\u20132238","journal-title":"IEEE Trans Ind Inf"},{"issue":"5","key":"277_CR5","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1016\/j.ymssp.2009.01.008","volume":"23","author":"Z Yan","year":"2009","unstructured":"Yan Z, Miyamoto A, Jiang Z (2009) Frequency slice wavelet transform for transient vibration response analysis. Mech Syst Signal Process 23(5):1474\u20131489","journal-title":"Mech Syst Signal Process"},{"issue":"4","key":"277_CR6","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1109\/TIA.2009.2018975","volume":"45","author":"SH Kia","year":"2009","unstructured":"Kia SH, Henao H, Capolino G-A (2009) Diagnosis of broken-bar fault in induction machines using discrete wavelet transform without slip estimation. IEEE Trans Ind Appl 45(4):1395\u20131404","journal-title":"IEEE Trans Ind Appl"},{"issue":"6","key":"277_CR7","doi-asserted-by":"publisher","first-page":"2083","DOI":"10.1016\/j.ymssp.2011.01.017","volume":"25","author":"F Al-Badour","year":"2011","unstructured":"Al-Badour F, Sunar M, Cheded L (2011) Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques. Mech Syst Signal Process 25(6):2083\u20132101","journal-title":"Mech Syst Signal Process"},{"issue":"1","key":"277_CR8","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.ymssp.2012.08.013","volume":"39","author":"JC Jauregui-Correa","year":"2013","unstructured":"Jauregui-Correa JC (2013) The effect of nonlinear traveling waves on rotating machinery. Mech Syst Signal Process 39(1):129\u2013142","journal-title":"Mech Syst Signal Process"},{"key":"277_CR9","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.isatra.2014.09.006","volume":"55","author":"Z Wang","year":"2015","unstructured":"Wang Z, Han Z, Gu F, Gu JX, Ning S (2015) A novel procedure for diagnosing multiple faults in rotating machinery. ISA Trans 55:208\u2013218","journal-title":"ISA Trans"},{"issue":"5","key":"277_CR10","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1098\/rsif.2005.0058","volume":"2","author":"JS Smith","year":"2005","unstructured":"Smith JS (2005) The local mean decomposition and its application to eeg perception data. J R Soc Interface 2(5):443\u2013454","journal-title":"J R Soc Interface"},{"issue":"2","key":"277_CR11","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.dsp.2011.09.008","volume":"22","author":"J Cheng","year":"2012","unstructured":"Cheng J, Yang Y, Yang Y (2012) A rotating machinery fault diagnosis method based on local mean decomposition. Digital Signal Process 22(2):356\u2013366","journal-title":"Digital Signal Process"},{"key":"277_CR12","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.mechmachtheory.2015.04.009","volume":"91","author":"Z Zheng","year":"2015","unstructured":"Zheng Z, Jiang W, Wang Z, Zhu Y, Yang K (2015) Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions. Mech Mach Theory 91:151\u2013167","journal-title":"Mech Mach Theory"},{"key":"277_CR13","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.dsp.2015.07.001","volume":"46","author":"Y Li","year":"2015","unstructured":"Li Y, Xu M, Haiyang Z, Wei Y, Huang W (2015) A new rotating machinery fault diagnosis method based on improved local mean decomposition. Digital Signal Process 46:201\u2013214","journal-title":"Digital Signal Process"},{"issue":"1","key":"277_CR14","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.jneumeth.2011.01.027","volume":"197","author":"S Ziad","year":"2011","unstructured":"Ziad S, Hojjat A (2011) Probabilistic neural networks for diagnosis of alzheimer\u2019s disease using conventional and wavelet coherence. J Neurosci Methods 197(1):165\u201370","journal-title":"J Neurosci Methods"},{"issue":"4","key":"277_CR15","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","volume":"27","author":"J Tang","year":"2016","unstructured":"Tang J, Deng C, Huang G-B (2016) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809\u2013821","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"277_CR16","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s12293-016-0185-2","volume":"9","author":"X Cheng","year":"2017","unstructured":"Cheng X, Liu H, Xu X, Sun F (2017) Denoising deep extreme learning machine for sparse representation. Memet Comput 9(3):199\u2013212","journal-title":"Memet Comput"},{"issue":"2","key":"277_CR17","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s12293-016-0182-5","volume":"9","author":"H Lu","year":"2017","unstructured":"Lu H, Du B, Liu J, Xia H, Yeap WK (2017) A kernel extreme learning machine algorithm based on improved particle swam optimization. Memet Comput 9(2):121\u2013128","journal-title":"Memet Comput"},{"key":"277_CR18","first-page":"1","volume":"3","author":"SP Das","year":"2016","unstructured":"Das SP, Padhy S (2016) Unsupervised extreme learning machine and support vector regression hybrid model for predicting energy commodity futures index. Memet Comput 3:1\u201314","journal-title":"Memet Comput"},{"issue":"42","key":"277_CR19","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2012","unstructured":"Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(42):513\u201329","journal-title":"IEEE Trans Syst Man Cybern Part B Cybern"},{"issue":"6","key":"277_CR20","doi-asserted-by":"publisher","first-page":"379","DOI":"10.3390\/en9060379","volume":"9","author":"Z-X Yang","year":"2016","unstructured":"Yang Z-X, Wang X-B, Zhong J-H (2016) Representational learning for fault diagnosis of wind turbine equipment: a multi-layered extreme learning machines approach. Energies 9(6):379","journal-title":"Energies"},{"issue":"99","key":"277_CR21","first-page":"1","volume":"PP","author":"Y Yang","year":"2016","unstructured":"Yang Y, Wu QJ, Wang Y (2016) Autoencoder with invertible functions for dimension reduction and image reconstruction. IEEE Trans Syst Man Cybern Syst PP(99):1\u201315","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"11","key":"277_CR22","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1109\/TCYB.2015.2481713","volume":"46","author":"Y Yang","year":"2016","unstructured":"Yang Y, Wu QJ (2016) Multilayer extreme learning machine with subnetwork nodes for representation learning. IEEE Trans Cybern 46(11):2570\u20132583","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"277_CR23","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501","journal-title":"Neurocomputing"},{"issue":"12","key":"277_CR24","doi-asserted-by":"publisher","first-page":"2885","DOI":"10.1109\/TCYB.2015.2492468","volume":"46","author":"Y Yang","year":"2016","unstructured":"Yang Y, Wu QJ (2016) Extreme learning machine with subnetwork hidden nodes for regression and classification. IEEE Trans Cybern 46(12):2885\u20132898","journal-title":"IEEE Trans Cybern"},{"issue":"8","key":"277_CR25","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1109\/TCYB.2014.2352594","volume":"45","author":"Y Yang","year":"2015","unstructured":"Yang Y, Wu QJ, Wang Y, Zeeshan K, Lin X, Yuan X (2015) Data partition learning with multiple extreme learning machines. IEEE Trans Cybern 45(8):1463\u20131475","journal-title":"IEEE Trans Cybern"},{"issue":"8","key":"277_CR26","doi-asserted-by":"publisher","first-page":"3419","DOI":"10.1109\/TIE.2013.2238871","volume":"60","author":"X Gong","year":"2013","unstructured":"Gong X, Qiao W (2013) Bearing fault diagnosis for direct-drive wind turbines via current-demodulated signals. IEEE Trans Ind Electron 60(8):3419\u20133428","journal-title":"IEEE Trans Ind Electron"},{"issue":"1971","key":"277_CR27","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"NE Huang","year":"1998","unstructured":"Huang NE (1998) Huang, n.e, et al.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. proc. r. soc. lond. a 454, 903\u2013995. Proc R Soc A Math Phys Eng Sci 454(1971):903\u2013995","journal-title":"Proc R Soc A Math Phys Eng Sci"}],"container-title":["Memetic Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-018-0277-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12293-018-0277-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-018-0277-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,11]],"date-time":"2019-12-11T19:12:29Z","timestamp":1576091549000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12293-018-0277-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,12]]},"references-count":27,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["277"],"URL":"https:\/\/doi.org\/10.1007\/s12293-018-0277-2","relation":{},"ISSN":["1865-9284","1865-9292"],"issn-type":[{"value":"1865-9284","type":"print"},{"value":"1865-9292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,12]]},"assertion":[{"value":"31 October 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}