{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T00:56:05Z","timestamp":1770512165170,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T00:00:00Z","timestamp":1595376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D plan of China","award":["2018YFB1201704"],"award-info":[{"award-number":["2018YFB1201704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>To quickly and effectively identify an axle box bearing fault of high-speed electric multiple units (EMUs), an evolutionary online sequential extreme learning machine (OS-ELM) fault diagnosis method for imbalanced data was proposed. In this scheme, the resampling scale is first determined according to the resampling empirical formulation, the K-means synthetic minority oversampling technique (SMOTE) method is then used for oversampling the minority class samples, a method based on Euclidean distance is applied for undersampling the majority class samples, and the complex data features are extracted from the reconstructed dataset. Second, the reconstructed dataset is input into the diagnosis model. Finally, the artificial bee colony (ABC) algorithm is used to globally optimize the combination of input weights, hidden layer bias, and the number of hidden layer nodes for an OS-ELM, and the diagnosis model is allowed to evolve. The proposed method was tested on the axle box bearing monitoring data of high-speed EMUs, on which the position of the axle box bearings was symmetrical. Numerical testing proved that the method has the characteristics of faster detection and higher classification performance regarding the minority class data compared to other standard and classical algorithms.<\/jats:p>","DOI":"10.3390\/sym12081204","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T11:26:01Z","timestamp":1595503561000},"page":"1204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Imbalanced Data Fault Diagnosis Based on an Evolutionary Online Sequential Extreme Learning Machine"],"prefix":"10.3390","volume":"12","author":[{"given":"Wei","family":"Hao","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Department of Information Technology, CRRC Qingdao Sifang Limited Company, Qingdao 266111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"823","DOI":"10.3390\/s20030823","article-title":"A Real-Time Fault Early Warning Method for a High-Speed EMU Axle Box Bearing","volume":"20","author":"Zhao","year":"2020","journal-title":"Sensors"},{"key":"ref_2","first-page":"1246","article-title":"Ensemble empirical mode decomposition and fuzzy entropy in fault feature analysis for high-speed train bogie","volume":"31","author":"Qin","year":"2014","journal-title":"Control Theory Appl."},{"key":"ref_3","first-page":"2311","article-title":"Failure diagnosis system for CRH3 electrical multiple unit","volume":"16","author":"Huang","year":"2010","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_4","first-page":"2450","article-title":"Improved parallel frequent pattern growth algorithm in EMU\u2019s fault diagnosis knowledge mining","volume":"22","author":"Zhou","year":"2016","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_5","first-page":"35","article-title":"Fault Diagnosis Based on Multichannel Vibration Signal for Port Machine Trolley Wheel Bearings","volume":"9","author":"Li","year":"2007","journal-title":"Bearing"},{"key":"ref_6","first-page":"30","article-title":"Research on fault diagnosis method for bogie bearings of Metro vehicle","volume":"37","author":"Liu","year":"2015","journal-title":"J. China Railw. Soc."},{"key":"ref_7","first-page":"1831","article-title":"Imbalanced data processing algorithm based on boundary mixed sampling","volume":"10","author":"Feng","year":"2017","journal-title":"Control Dec."},{"key":"ref_8","first-page":"50","article-title":"Analysis on temperature distribution of failure axle box bearings of high-speed train","volume":"38","author":"Wuchu","year":"2016","journal-title":"J. China Railw. Soc."},{"key":"ref_9","first-page":"97","article-title":"Fault Prediction Based on PSO\u2014BP Neural Network About Wheel and Axle Box of Bogie in Urban Rail Train","volume":"12","author":"Yin","year":"2015","journal-title":"Complex Syst. Complex. Sci."},{"key":"ref_10","first-page":"41","article-title":"An intelligent fault diagnosis method for bogie bearings of train based on wavelet packet decomposition and EEMD","volume":"37","author":"Liu","year":"2015","journal-title":"J. China Railw. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","article-title":"A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks","volume":"17","author":"Liang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_12","first-page":"2808","article-title":"Objective-driven dynamic dispatching rule for semiconductor wafer fabrication facilities","volume":"20","author":"Jia","year":"2014","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"25542","DOI":"10.1109\/ACCESS.2017.2766675","article-title":"Experimental study on extreme learning machine applications for speech enhancement","volume":"5","author":"Hussain","year":"2017","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"160043","DOI":"10.1109\/ACCESS.2019.2947843","article-title":"A Novel Machine Learning Method Based Approach for Li-Ion Battery Prognostic and Health Management","volume":"7","author":"Fan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.1007\/s11042-016-3559-z","article-title":"A pathological brain detection system based on kernel based ELM","volume":"77","author":"Lu","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1016\/j.patcog.2005.03.028","article-title":"Evolutionary extreme learning machine","volume":"38","author":"Zhu","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.asoc.2015.04.045","article-title":"Software defect prediction using cost-sensitive neural network","volume":"33","author":"Arar","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.bbe.2016.08.002","article-title":"Feature projection k-NN classifier model for imbalanced and incomplete medical data","volume":"36","author":"Porwik","year":"2016","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.ins.2017.12.030","article-title":"Francesca Perla Using generative adversarial networks for improving classification effectiveness in credit card fraud detection","volume":"479","author":"Fiore","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.neucom.2014.06.021","article-title":"A resampling ensemble algorithm for classification of imbalance problems","volume":"143","author":"Qian","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, J., Xu, Z., and Che, Y. (2019). Power Quality Disturbance Classification Based on DWT and Multilayer Perceptron Extreme Learning Machine. Appl. Sci., 9.","DOI":"10.3390\/app9112315"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10761","DOI":"10.1016\/j.measurement.2020.107616","article-title":"Bearing Fault Diagnosis with Varying Conditions using Angular Domain Resampling Technology, SDP and DCNN","volume":"156","author":"Gu","year":"2020","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.dss.2017.11.006","article-title":"A synthetic informative minority over-sampling (SIMO) algorithm leveraging support vector machine to enhance learning from imbalanced datasets","volume":"106","author":"Piri","year":"2018","journal-title":"Decis. Support Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.knosys.2011.06.013","article-title":"On the effectiveness of preprocessing methods when dealing with different levels of class imbalance","volume":"25","author":"Mollineda","year":"2012","journal-title":"Knowl. Based Syst."},{"key":"ref_25","first-page":"2613","article-title":"Under-Sampling method based on sample weight for imbalanced data","volume":"53","author":"Xiong","year":"2016","journal-title":"J. Comput. Res. Dev."},{"key":"ref_26","first-page":"1773","article-title":"Zero-order TSK-type fuzzy system for imbalanced data classification","volume":"43","author":"Gu","year":"2017","journal-title":"Acta Autom. Sin."},{"key":"ref_27","first-page":"252","article-title":"A fuzzy support vector machine algorithm for imbalanced data classification","volume":"56","author":"Ju","year":"2016","journal-title":"J. Dalian Univ. Technol."},{"key":"ref_28","first-page":"513","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","article-title":"A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm","volume":"39","author":"Karaboga","year":"2007","journal-title":"J. Glob. Optim."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/8\/1204\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:50:53Z","timestamp":1760176253000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/8\/1204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,22]]},"references-count":29,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["sym12081204"],"URL":"https:\/\/doi.org\/10.3390\/sym12081204","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,22]]}}}