{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T10:45:21Z","timestamp":1769942721294,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T00:00:00Z","timestamp":1573430400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T00:00:00Z","timestamp":1573430400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"University og Guilan"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s00500-019-04516-z","type":"journal-article","created":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T11:02:56Z","timestamp":1573470176000},"page":"10005-10023","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A new intelligent fault diagnosis method for bearing in different speeds based on the FDAF-score algorithm, binary particle swarm optimization, and support vector machine"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0744-7919","authenticated-orcid":false,"given":"Saeed","family":"Nezamivand Chegini","sequence":"first","affiliation":[]},{"given":"Ahmad","family":"Bagheri","sequence":"additional","affiliation":[]},{"given":"Farid","family":"Najafi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,11]]},"reference":[{"key":"4516_CR1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.apacoust.2014.08.016","volume":"89","author":"JB Ali","year":"2015","unstructured":"Ali JB, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F (2015) Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 89:16\u201327. https:\/\/doi.org\/10.1016\/j.apacoust.2014.08.016","journal-title":"Appl Acoust"},{"key":"4516_CR2","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.jsv.2017.02.041","volume":"397","author":"I Attoui","year":"2017","unstructured":"Attoui I, Fergani N, Boutasseta N, Oudjani B, Deliou A (2017) A new time\u2013frequency method for identification and classification of ball bearing faults. J Sound Vib 397:241\u2013265. https:\/\/doi.org\/10.1016\/j.jsv.2017.02.041","journal-title":"J Sound Vib"},{"key":"4516_CR3","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.patrec.2014.10.007","volume":"52","author":"H Banka","year":"2015","unstructured":"Banka H, Dara S (2015) A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation. Pattern Recognit Lett 52:94\u2013100. https:\/\/doi.org\/10.1016\/j.patrec.2014.10.007","journal-title":"Pattern Recognit Lett"},{"key":"4516_CR4","unstructured":"Bearing Data Center (2016) Case Western Reserve University. http:\/\/csegroups.case.edu\/bearingdatacenter\/home"},{"key":"4516_CR5","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.asoc.2015.06.060","volume":"36","author":"HK Bhuyan","year":"2015","unstructured":"Bhuyan HK, Kamila NK (2015) Privacy preserving sub-feature selection in distributed data mining. Appl Soft Comput 36:552\u2013569. https:\/\/doi.org\/10.1016\/j.asoc.2015.06.060","journal-title":"Appl Soft Comput"},{"key":"4516_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.measurement.2014.04.024","volume":"55","author":"DJ Bordoloi","year":"2014","unstructured":"Bordoloi DJ, Tiwari R (2014a) Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time\u2013frequency vibration data. Measurement 55:1\u201314. https:\/\/doi.org\/10.1016\/j.measurement.2014.04.024","journal-title":"Measurement"},{"key":"4516_CR7","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.mechmachtheory.2013.10.006","volume":"73","author":"DJ Bordoloi","year":"2014","unstructured":"Bordoloi DJ, Tiwari R (2014b) Optimum multi-fault classification of gears with integration of evolutionary and SVM algorithms. Mech Mach Theory 73:49\u201360. https:\/\/doi.org\/10.1016\/j.mechmachtheory.2013.10.006","journal-title":"Mech Mach Theory"},{"key":"4516_CR8","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/978-3-319-06590-8_43","volume-title":"Proceedings of the 9th IFToMM International Conference on Rotor Dynamics","author":"D. J. Bordoloi","year":"2015","unstructured":"Bordoloi DJ, Tiwari R (2015) Optimisation of SVM methodology for multiple fault taxonomy of rolling bearings from acceleration records. In: Proceedings of the 9th IFToMM international conference on rotor dynamics, pp 533\u2013542. https:\/\/doi.org\/10.1007\/978-3-319-06590-8_43"},{"key":"4516_CR9","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.neucom.2015.01.070","volume":"159","author":"G Chen","year":"2015","unstructured":"Chen G, Chen J (2015) A novel wrapper method for feature selection and its applications. Neurocomputing 159:219\u2013226. https:\/\/doi.org\/10.1016\/j.neucom.2015.01.070","journal-title":"Neurocomputing"},{"key":"4516_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-09918-7_90","author":"S Fatima","year":"2015","unstructured":"Fatima S, Mohanty AR, Naikan VNA (2015) Multiple fault classification using support vector machine in a machinery fault simulator. Vib Eng Technol Mach. https:\/\/doi.org\/10.1007\/978-3-319-09918-7_90","journal-title":"Vib Eng Technol Mach"},{"issue":"2078","key":"4516_CR11","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1098\/rspa.2006.1761","volume":"463","author":"MG Frei","year":"2007","unstructured":"Frei MG, Osorio I (2007) Intrinsic time-scale decomposition: time-frequency\u2013energy analysis and real-time filtering of non-stationary signals. Proc R Soc Lond A Math Phys Sci 463(2078):321\u2013342. https:\/\/doi.org\/10.1098\/rspa.2006.1761","journal-title":"Proc R Soc Lond A Math Phys Sci"},{"key":"4516_CR12","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/3264969","author":"W Fu","year":"2019","unstructured":"Fu W, Tan J, Zhang X, Chen T, Wang K (2019) Blind parameter identification of MAR model and mutation hybrid GWO-SCA optimized SVM for fault diagnosis of rotating machinery. Complexity. https:\/\/doi.org\/10.1155\/2019\/3264969","journal-title":"Complexity"},{"issue":"16","key":"4516_CR13","doi-asserted-by":"publisher","first-page":"3999","DOI":"10.1109\/TSP.2013.2265222","volume":"61","author":"J Gilles","year":"2013","unstructured":"Gilles J (2013) Empirical wavelet transform. IEEE T Signal Process 61(16):3999\u20134010. https:\/\/doi.org\/10.1109\/TSP.2013.2265222","journal-title":"IEEE T Signal Process"},{"key":"4516_CR14","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.apacoust.2017.05.018","volume":"127","author":"T Guo","year":"2017","unstructured":"Guo T, Deng Z (2017) An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing. Appl Acoust 127:46\u201362. https:\/\/doi.org\/10.1016\/j.apacoust.2017.05.018","journal-title":"Appl Acoust"},{"issue":"1\u20133","key":"4516_CR15","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1\u20133):389\u2013422. https:\/\/doi.org\/10.1023\/A:1012487302797","journal-title":"Mach Learn"},{"issue":"2","key":"4516_CR16","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1109\/72.991427","volume":"13","author":"CW Hsu","year":"2002","unstructured":"Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415\u2013425. https:\/\/doi.org\/10.1109\/72.991427","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"4516_CR17","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.eswa.2005.09.024","volume":"31","author":"CL Huang","year":"2006","unstructured":"Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31(2):231\u2013240. https:\/\/doi.org\/10.1016\/j.eswa.2005.09.024","journal-title":"Expert Syst Appl"},{"issue":"1971","key":"4516_CR18","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"NE Huang","year":"1998","unstructured":"Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Sci 454(1971):903\u2013995. https:\/\/doi.org\/10.1098\/rspa.1998.0193","journal-title":"Proc R Soc Lond A Math Phys Sci"},{"key":"4516_CR19","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"JS Jang","year":"1993","unstructured":"Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665\u2013685. https:\/\/doi.org\/10.1109\/21.256541","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"4516_CR20","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.asoc.2015.02.015","volume":"30","author":"L Jedli\u0144ski","year":"2015","unstructured":"Jedli\u0144ski L, Jonak J (2015) Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform. Appl Soft Comput 30:636\u2013641. https:\/\/doi.org\/10.1016\/j.asoc.2015.02.015","journal-title":"Appl Soft Comput"},{"issue":"2","key":"4516_CR21","doi-asserted-by":"publisher","first-page":"025003","DOI":"10.1088\/0957-0233\/25\/2\/025003","volume":"25","author":"F Jiang","year":"2013","unstructured":"Jiang F, Zhu Z, Li W, Chen G, Zhou G (2013) Robust condition monitoring and fault diagnosis of rolling element bearings using improved EEMD and statistical features. Meas Sci Technol 25(2):025003. https:\/\/doi.org\/10.1088\/0957-0233\/25\/2\/025003","journal-title":"Meas Sci Technol"},{"key":"4516_CR22","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on systems, man, and cybernetics. Computational cybernetics and simulation, vol 5, pp 4104\u20134108. IEEE. https:\/\/doi.org\/10.1109\/icsmc.1997.637339","DOI":"10.1109\/icsmc.1997.637339"},{"issue":"12","key":"4516_CR23","doi-asserted-by":"publisher","first-page":"125701","DOI":"10.1088\/0957-0233\/20\/12\/125701","volume":"20","author":"Y Lei","year":"2009","unstructured":"Lei Y, Zuo MJ (2009) Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs. Meas Sci Technol 20(12):125701. https:\/\/doi.org\/10.1088\/0957-0233\/20\/12\/125701","journal-title":"Meas Sci Technol"},{"key":"4516_CR24","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1177\/0954406215624126","volume":"10","author":"Y Lei","year":"2017","unstructured":"Lei Y, Liu Z, Ouazri J, Lin J (2017) A fault diagnosis method of rolling element bearings based on CEEMDAN. Proc Inst Mech Eng C 10:1804\u20131815. https:\/\/doi.org\/10.1177\/0954406215624126","journal-title":"Proc Inst Mech Eng C"},{"issue":"1","key":"4516_CR25","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.measurement.2012.06.013","volume":"46","author":"Z Li","year":"2013","unstructured":"Li Z, Yan X, Tian Z, Yuan C, Peng Z, Li L (2013) Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis. Measurement 46(1):259\u2013271. https:\/\/doi.org\/10.1016\/j.measurement.2012.06.013","journal-title":"Measurement"},{"key":"4516_CR26","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.measurement.2014.12.021","volume":"63","author":"Y Li","year":"2015","unstructured":"Li Y, Xu M, Wei Y, Huang W (2015) An improvement EMD method based on the optimized rational Hermite interpolation approach and its application to gear fault diagnosis. Measurement 63:330\u2013345. https:\/\/doi.org\/10.1016\/j.measurement.2014.12.021","journal-title":"Measurement"},{"key":"4516_CR27","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.jsv.2015.01.037","volume":"344","author":"L Lu","year":"2015","unstructured":"Lu L, Yan J, de Silva CW (2015) Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition. J Sound Vib 344:464\u2013483. https:\/\/doi.org\/10.1016\/j.jsv.2015.01.037","journal-title":"J Sound Vib"},{"issue":"7","key":"4516_CR28","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/34.192463","volume":"11","author":"SG Mallat","year":"1989","unstructured":"Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674\u2013693. https:\/\/doi.org\/10.1109\/34.192463","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4516_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.09.019","volume-title":"PSOSCALF: a new hybrid PSO based on sine cosine algorithm and Levy flight for solving optimization problems","author":"S Nezamivand Chegini","year":"2018","unstructured":"Nezamivand Chegini S, Bagheri A, Najafi F (2018) PSOSCALF: a new hybrid PSO based on sine cosine algorithm and Levy flight for solving optimization problems. Soft Comput, Appl. https:\/\/doi.org\/10.1016\/j.asoc.2018.09.019"},{"key":"4516_CR30","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.measurement.2019.05.049","volume":"144","author":"S Nezamivand Chegini","year":"2019","unstructured":"Nezamivand Chegini S, Bagheri A, Najafi F (2019) Application of a new EWT-based denoising technique in bearing fault diagnosis. Measurement 144:275\u2013297. https:\/\/doi.org\/10.1016\/j.measurement.2019.05.049","journal-title":"Measurement"},{"issue":"22","key":"4516_CR31","doi-asserted-by":"publisher","first-page":"9024","DOI":"10.1016\/j.eswa.2015.07.064","volume":"42","author":"P Nguyen","year":"2015","unstructured":"Nguyen P, Kang M, Kim JM, Ahn BH, Ha JM, Choi BK (2015) Robust condition monitoring of rolling element bearings using de-noising and envelope analysis with signal decomposition techniques. Expert Syst Appl 42(22):9024\u20139032. https:\/\/doi.org\/10.1016\/j.eswa.2015.07.064","journal-title":"Expert Syst Appl"},{"issue":"5","key":"4516_CR32","doi-asserted-by":"publisher","first-page":"055002","DOI":"10.1088\/1361-6501\/ab0473","volume":"30","author":"Y Shan","year":"2019","unstructured":"Shan Y, Zhou J, Jiang W, Liu J, Xu Y, Zhao Y (2019) A fault diagnosis method for rotating machinery based on improved variational mode decomposition and a hybrid artificial sheep algorithm. Meas Sci Technol 30(5):055002","journal-title":"Meas Sci Technol"},{"key":"4516_CR33","doi-asserted-by":"publisher","unstructured":"Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: International conference on evolutionary computation proceedings. IEEE world congress on computational intelligence, pp 69\u201373. IEEE. https:\/\/doi.org\/10.1109\/ICEC.1998.699146","DOI":"10.1109\/ICEC.1998.699146"},{"key":"4516_CR34","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.eswa.2017.02.049","volume":"81","author":"Q Song","year":"2017","unstructured":"Song Q, Jiang H, Liu J (2017) Feature selection based on FDA and F-score for multi-class classification. Expert Syst Appl 81:22\u201327. https:\/\/doi.org\/10.1016\/j.eswa.2017.02.049","journal-title":"Expert Syst Appl"},{"key":"4516_CR35","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1109\/72.572108","volume":"8","author":"A Sperduti","year":"1997","unstructured":"Sperduti A, Starita A (1997) Supervised neural networks for the classification of structures. IEEE Trans Neural Netw 8:714\u2013735","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"4516_CR36","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1007\/s11012-014-9968-z","volume":"50","author":"A Tabrizi","year":"2015","unstructured":"Tabrizi A, Garibaldi L, Fasana A, Marchesiello S (2015) Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine. Meccanica 50(3):865\u2013874. https:\/\/doi.org\/10.1007\/s11012-014-9968-z","journal-title":"Meccanica"},{"key":"4516_CR37","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.proeng.2016.05.142","volume":"144","author":"V Vakharia","year":"2016","unstructured":"Vakharia V, Gupta VK, Kankar PK (2016) Bearing fault diagnosis using feature ranking methods and fault identification algorithms. Proc Eng 144:343\u2013350. https:\/\/doi.org\/10.1016\/j.proeng.2016.05.142","journal-title":"Proc Eng"},{"key":"4516_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"1995","unstructured":"Vapnik V (1995) The nature of statistical learning theory. Springer, New York"},{"issue":"2","key":"4516_CR39","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1007\/s11771-017-3450-8","volume":"24","author":"CS Wang","year":"2017","unstructured":"Wang CS, Sha CY, Su M, Hu YK (2017) An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter. J Cent South Univ 24(2):478\u2013488. https:\/\/doi.org\/10.1007\/s11771-017-3450-8","journal-title":"J Cent South Univ"},{"key":"4516_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2016.10.022","volume":"116","author":"Z Wei","year":"2017","unstructured":"Wei Z, Wang Y, He S, Bao J (2017) A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection. Knowl Based Syst 116:1\u201312. https:\/\/doi.org\/10.1016\/j.knosys.2016.10.022","journal-title":"Knowl Based Syst"},{"key":"4516_CR41","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.ymssp.2015.03.002","volume":"62","author":"X Xue","year":"2015","unstructured":"Xue X, Zhou J, Xu Y, Zhu W, Li C (2015) An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis. Mech Syst Signal Process 62:444\u2013459. https:\/\/doi.org\/10.1016\/j.ymssp.2015.03.002","journal-title":"Mech Syst Signal Process"},{"issue":"3","key":"4516_CR42","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neucom.2018.05.002","volume":"313","author":"X Yan","year":"2018","unstructured":"Yan X, Jia M (2018) A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing 313(3):47\u201364. https:\/\/doi.org\/10.1016\/j.neucom.2018.05.002","journal-title":"Neurocomputing"},{"issue":"4","key":"4516_CR43","first-page":"731","volume":"5","author":"H Yin","year":"2014","unstructured":"Yin H, Qiao J, Fu P, Xia XY (2014) Face feature selection with binary particle swarm optimization and support vector machine. J Inf Hiding Multimed Signal Process 5(4):731\u2013739","journal-title":"J Inf Hiding Multimed Signal Process"},{"issue":"8","key":"4516_CR44","doi-asserted-by":"publisher","first-page":"2656","DOI":"10.1016\/j.patcog.2015.02.025","volume":"48","author":"Z Zeng","year":"2015","unstructured":"Zeng Z, Zhang H, Zhang R, Yin C (2015) A novel feature selection method considering feature interaction. Pattern Recognit 48(8):2656\u20132666. https:\/\/doi.org\/10.1016\/j.patcog.2015.02.025","journal-title":"Pattern Recognit"},{"key":"4516_CR45","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.neucom.2015.04.069","volume":"167","author":"X Zhang","year":"2015","unstructured":"Zhang X, Chen W, Wang B, Chen X (2015) Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization. Neurocomputing 167:260\u2013279. https:\/\/doi.org\/10.1016\/j.neucom.2015.04.069","journal-title":"Neurocomputing"},{"key":"4516_CR46","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.1016\/j.neucom.2017.11.016","volume":"275","author":"X Zhang","year":"2018","unstructured":"Zhang X, Zhang Q, Chen M, Sun Y, Qin X, Li H (2018) A two-stage feature selection and intelligent fault diagnosis method for rotating machinery using hybrid filter and wrapper method. Neurocomputing 275:2426\u20132439. https:\/\/doi.org\/10.1016\/j.neucom.2017.11.016","journal-title":"Neurocomputing"},{"issue":"7","key":"4516_CR47","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.1007\/s11771-016-3222-x","volume":"23","author":"DZ Zhao","year":"2016","unstructured":"Zhao DZ, Li JY, Cheng WD, Wang TY, Wen WG (2016) Rolling element bearing instantaneous rotational frequency estimation based on EMD soft-thresholding denoising and instantaneous fault characteristic frequency. J Cent South Univ 23(7):1682\u20131689. https:\/\/doi.org\/10.1007\/s11771-016-3222-x","journal-title":"J Cent South Univ"},{"issue":"2","key":"4516_CR48","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/s10845-014-0987-3","volume":"28","author":"R Ziani","year":"2017","unstructured":"Ziani R, Felkaoui A, Zegadi R (2017a) Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher\u2019s criterion. J Intell Manuf 28(2):405\u2013417. https:\/\/doi.org\/10.1007\/s10845-014-0987-3","journal-title":"J Intell Manuf"},{"key":"4516_CR49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-96181-1_1","author":"R Ziani","year":"2017","unstructured":"Ziani R, Mahgoun H, Fedala S, Felkaoui A (2017b) Feature selection scheme based on Pareto method for gearbox fault diagnosis. Signal Process Appl Rotating Mach Diagn. https:\/\/doi.org\/10.1007\/978-3-319-96181-1_1","journal-title":"Signal Process Appl Rotating Mach Diagn"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-019-04516-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-019-04516-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-019-04516-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T00:28:41Z","timestamp":1604968121000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-019-04516-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,11]]},"references-count":49,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["4516"],"URL":"https:\/\/doi.org\/10.1007\/s00500-019-04516-z","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,11]]},"assertion":[{"value":"11 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We declare we have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}