{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T11:19:20Z","timestamp":1763810360476,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Prog Artif Intell"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s13748-020-00217-z","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T07:02:36Z","timestamp":1601535756000},"page":"341-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Bearing fault diagnostic using machine learning algorithms"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3563-5068","authenticated-orcid":false,"given":"Laith S.","family":"Sawaqed","sequence":"first","affiliation":[]},{"given":"Ayman M.","family":"Alrayes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,1]]},"reference":[{"key":"217_CR1","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.ijpe.2004.06.057","volume":"105","author":"I Alsyouf","year":"2007","unstructured":"Alsyouf, I.: The role of maintenance in improving companies\u2019 productivity and profitability. Int. J. Prod. Econ. 105, 70\u201378 (2007)","journal-title":"Int. J. Prod. Econ."},{"issue":"3","key":"217_CR2","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1108\/13552510110404503","volume":"7","author":"B Al-Najjar","year":"2001","unstructured":"Al-Najjar, B., Wang, W.: A conceptual model for fault detection and decision making for rolling element bearings in paper mills. J. Qual. Maint. Eng. 7(3), 192\u2013206 (2001)","journal-title":"J. Qual. Maint. Eng."},{"issue":"2","key":"217_CR3","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/MIE.2013.2287651","volume":"8","author":"H Henao","year":"2014","unstructured":"Henao, H., et al.: Trends in fault diagnosis for electrical machines: a review of diagnostic techniques. IEEE Ind. Electron. Mag. 8(2), 31\u201342 (2014)","journal-title":"IEEE Ind. Electron. Mag."},{"issue":"1","key":"217_CR4","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/MIA.2007.909802","volume":"14","author":"AH Bonnett","year":"2008","unstructured":"Bonnett, A.H., Yung, C.: Increased efficiency versus increased reliability. IEEE Ind. Appl. Mag. 14(1), 29\u201336 (2008)","journal-title":"IEEE Ind. Appl. Mag."},{"issue":"16","key":"217_CR5","doi-asserted-by":"publisher","first-page":"3246","DOI":"10.3390\/app9163246","volume":"9","author":"D Wu","year":"2019","unstructured":"Wu, D., Wang, H., Liu, H., He, T., Xie, T.: Health monitoring on the spacecraft bearings in high-speed rotating systems by using the clustering fusion of normal acoustic parameters. Appl. Sci. 9(16), 3246 (2019)","journal-title":"Appl. Sci."},{"issue":"2","key":"217_CR6","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1006\/mssp.2001.1462","volume":"17","author":"B Samanta","year":"2003","unstructured":"Samanta, B., Al-Balushi, K.R.: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Syst. Signal Process. 17(2), 317\u2013328 (2003)","journal-title":"Mech. Syst. Signal Process."},{"issue":"3","key":"217_CR7","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.1016\/j.eswa.2010.07.119","volume":"38","author":"PK Kankar","year":"2011","unstructured":"Kankar, P.K., Sharma, S.C., Harsha, S.P.: Fault diagnosis of ball bearings using machine learning methods. Expert Syst. Appl. 38(3), 1876\u20131886 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"217_CR8","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1177\/1077546310366264","volume":"17","author":"G Marichal","year":"2011","unstructured":"Marichal, G., Artes, M., Garcia-Prada, J.: An intelligent system for faulty-bearing detection based on vibration spectra. J. Vib. Control 17(6), 931\u2013942 (2011)","journal-title":"J. Vib. Control"},{"key":"217_CR9","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.promfg.2018.02.036","volume":"20","author":"A Khadersab","year":"2018","unstructured":"Khadersab, A., Shivakumar, S.: Vibration analysis techniques for rotating machinery and its vibration analysis effect techniques for rotating on bearing faults machinery and its effect on bearing faults costing models for capacity optimization in industry 4. 0: trade-off between used capacity and operational efficiency. Procedia Manuf. 20, 247\u2013252 (2018)","journal-title":"Procedia Manuf."},{"key":"217_CR10","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/S0963-8695(01)00044-5","volume":"35","author":"NG Nikolaou","year":"2002","unstructured":"Nikolaou, N.G., Antoniadis, I.A.: Rolling element bearing fault diagnosis using wavelet packets. Ndt & E Int. 35, 197\u2013205 (2002)","journal-title":"Ndt & E Int."},{"key":"217_CR11","doi-asserted-by":"crossref","unstructured":"Soualhi, A., Hawwari, Y., Medjaher, K., Guy, C., Razik, H., Guillet, F.: PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes. Int. J. Prognostics Health Manage. 9, (2018)","DOI":"10.36001\/ijphm.2018.v9i2.2736"},{"key":"217_CR12","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1016\/j.ymssp.2005.09.012","volume":"20","author":"AKS Jardine","year":"2006","unstructured":"Jardine, A.K.S., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal. Process. 20, 1483\u20131510 (2006)","journal-title":"Mech. Syst. Signal. Process."},{"key":"217_CR13","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1006\/mssp.1996.0041","volume":"10","author":"TI Liu","year":"1996","unstructured":"Liu, T.I., Singonahalli, J.H., Iyer, N.R.: Detection of roller bearing defects using expert system and fuzzy logic. Mech. Syst. Signal Process. 10, 595\u2013614 (1996)","journal-title":"Mech. Syst. Signal Process."},{"issue":"2","key":"217_CR14","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/41.222642","volume":"40","author":"IE Alguindigue","year":"1993","unstructured":"Alguindigue, I.E., Loskiewicz-Buczak, A., Uhrig, R.E.: Monitoring and diagnosis of rolling element bearings using artificial neural networks. IEEE Trans. Ind. Electron. 40(2), 209\u2013217 (1993)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"3","key":"217_CR15","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1049\/ip-vis:20000325","volume":"147","author":"LB Jack","year":"2000","unstructured":"Jack, L.B., Nandi, A.K.: Genetic algorithms for feature selection in machine condition monitoring with vibration signals. IEE Proc. Vis. Image Signal Process. 147(3), 205\u2013212 (2000)","journal-title":"IEE Proc. Vis. Image Signal Process."},{"key":"217_CR16","doi-asserted-by":"publisher","DOI":"10.2172\/1557163","volume-title":"Bearing fault detection and wear estimation using machine learning","author":"CD Jenkins","year":"2019","unstructured":"Jenkins, C.D.: Bearing fault detection and wear estimation using machine learning. Los Alamos National Lab (LANL), Los Alamos (2019)"},{"key":"217_CR17","doi-asserted-by":"crossref","unstructured":"Lessmeier, C., Kimotho, J.K., Zimmer, D., Sextro, W.: Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification. In: European Conference of the Prognostics and Health Management Society 2016, no. July, p. 17 (2016)","DOI":"10.36001\/phme.2016.v3i1.1577"},{"issue":"4","key":"217_CR18","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1109\/TIE.2008.917108","volume":"55","author":"M Blodt","year":"2008","unstructured":"Blodt, M., Granjon, P., Raison, B., Rostaing, G.: Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans. Ind. Electron. 55(4), 1813\u20131822 (2008)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"217_CR19","doi-asserted-by":"publisher","unstructured":"Zarei, J., Poshtan, J.: An advanced Park\u2019s vectors approach for bearing fault detection. IEEE Int. Conf. Ind. Technol. Mumbai pp. 1472-1479 (2006). https:\/\/doi.org\/10.1109\/ICIT.2006.372562","DOI":"10.1109\/ICIT.2006.372562"},{"key":"217_CR20","unstructured":"Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., Varnier, C.: PRONOSTIA: an experimental platform for bearings accelerated degradation tests. IEEE Int. Conf. Prognostics Health Manag. PHM'12., Denver, Colorado, United States. pp.1\u20138. (hal- 00719503) (2012)"},{"issue":"1","key":"217_CR21","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/0022-460X(84)90595-9","volume":"96","author":"PD McFadden","year":"1984","unstructured":"McFadden, P.D., Smith, J.D.: Model for the vibration produced by a single point defect in a rolling element bearing. J. Sound Vib. 96(1), 69\u201382 (1984)","journal-title":"J. Sound Vib."},{"key":"217_CR22","unstructured":"Stack, J.R., Habetler, T.G., Harley, R.G.: Fault classification and fault signature production for rolling element bearings in electric machines. In: IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2003\u2014Proceedings, vol. 40, no. 3, pp. 172\u2013176 (2003)"},{"issue":"1","key":"217_CR23","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1109\/TE.2002.808234","volume":"46","author":"SA McInerny","year":"2003","unstructured":"McInerny, S.A., Dai, Y.: Basic vibration signal processing for bearing fault detection. IEEE Trans. Educ. 46(1), 149\u2013156 (2003)","journal-title":"IEEE Trans. Educ."},{"issue":"8","key":"217_CR24","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/S0301-679X(99)00077-8","volume":"32","author":"N Tondon","year":"1999","unstructured":"Tondon, N., Choudhury, A.: A review of vibration and acoustics measurement methods for the detection of defects in rolling element bearing. Tribol. Int. 32(8), 469\u2013480 (1999)","journal-title":"Tribol. Int."},{"issue":"6","key":"217_CR25","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1109\/28.475697","volume":"31","author":"RG Bartheld","year":"1995","unstructured":"Bartheld, R.G., Habetler, T.G., Kamran, F.: Motor bearing damage detection using stator current monitoring. IEEE Trans. Ind. Appl. 31(6), 1274\u20131279 (1995)","journal-title":"IEEE Trans. Ind. Appl."},{"key":"217_CR26","unstructured":"Index of \/kat\/BearingDataCenter: https:\/\/groups.uni-paderborn.de\/kat\/BearingDataCenter\/ (2016). Accessed 02 March 2019"},{"issue":"7\u20138","key":"217_CR27","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1016\/j.engappai.2003.09.006","volume":"16","author":"B Samanta","year":"2003","unstructured":"Samanta, B., Al-Balushi, K.R., Al-Araimi, S.A.: Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng. Appl. Artif. Intell. 16(7\u20138), 657\u2013665 (2003)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"217_CR28","unstructured":"Obaid, R.R., Habetler, T.G., Stack, J.R.: Stator current analysis for bearing damage detection in induction motors. In: Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, no. August (2003)"},{"key":"217_CR29","volume-title":"Neural network design","author":"HB Demuth","year":"2014","unstructured":"Demuth, H.B., Beale, M.H., De Jess, O., Hagan, M.T.: Neural network design. Martin Hagan, Stillwater (2014)"},{"key":"217_CR30","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1115\/1.3269216","volume":"106","author":"J Mathew","year":"1984","unstructured":"Mathew, J.: The condition monitoring of rolling element bearings using vibration analysis. J. Vib. Acoust. 106, 447\u2013453 (1984)","journal-title":"J. Vib. Acoust."},{"issue":"12","key":"217_CR31","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1016\/S0301-679X(02)00063-4","volume":"35","author":"S Prabhakar","year":"2002","unstructured":"Prabhakar, S., Mohanty, A.R., Sekhar, A.S.: Application of discrete wavelet transform for detection of ball bearing race faults. Tribol. Int. 35(12), 793\u2013800 (2002)","journal-title":"Tribol. Int."},{"issue":"4\u20135","key":"217_CR32","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1016\/j.jsv.2007.01.001","volume":"302","author":"H Ocak","year":"2007","unstructured":"Ocak, H., Loparo, K.A., Discenzo, F.M.: Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics. J. Sound Vib. 302(4\u20135), 951\u2013961 (2007)","journal-title":"J. Sound Vib."},{"key":"217_CR33","doi-asserted-by":"crossref","unstructured":"Kimotho, J.K., Sextro, W.: An approach for feature extraction and selection from non-trending data for machinery prognosis. In: Proceedings of the Second European Conference of the Prognostics and Health Management Society, pp. 1\u20138 (2014)","DOI":"10.36001\/phme.2014.v2i1.1462"},{"issue":"3","key":"217_CR34","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/S0888-3270(03)00020-7","volume":"18","author":"B Samanta","year":"2004","unstructured":"Samanta, B.: Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech. Syst. Signal Process. 18(3), 625\u2013644 (2004)","journal-title":"Mech. Syst. Signal Process."},{"key":"217_CR35","unstructured":"Genetic Algorithm Options\u2014MATLAB & Simulink: https:\/\/www.mathworks.com\/help\/gads\/genetic-algorithm-options.html (2019). Accessed 2 March 2019"},{"key":"217_CR36","doi-asserted-by":"publisher","DOI":"10.21917\/ijsc.2015.0150","author":"A Umbarkar","year":"2015","unstructured":"Umbarkar, A., Sheth, P.: Crossover operators in genetic algorithms: a review. ICTACT J Soft Comput (2015). https:\/\/doi.org\/10.21917\/ijsc.2015.0150","journal-title":"ICTACT J Soft Comput"},{"issue":"2","key":"217_CR37","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.amc.2009.02.044","volume":"212","author":"K Deep","year":"2009","unstructured":"Deep, K., Pratap, K., Kansal, M.L., Mohan, C.: A real coded genetic algorithm for solving integer and mixed integer optimization problems. Appl. Math. Comput. 212(2), 505\u2013518 (2009)","journal-title":"Appl. Math. Comput."},{"key":"217_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-03315-9","volume-title":"Genetic Algorithms + Data Structures = Evolution Programs","author":"Z Michalewicz","year":"1996","unstructured":"Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Berlin (1996)","edition":"3"},{"key":"217_CR39","doi-asserted-by":"crossref","unstructured":"Wang, B.: Data fused motor fault identification based on adversarial auto-encoder. In: 2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), pp. 299\u2013305 (2019)","DOI":"10.1109\/PEDG.2019.8807538"}],"container-title":["Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-020-00217-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13748-020-00217-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-020-00217-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T06:44:46Z","timestamp":1723704286000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13748-020-00217-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,1]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["217"],"URL":"https:\/\/doi.org\/10.1007\/s13748-020-00217-z","relation":{},"ISSN":["2192-6352","2192-6360"],"issn-type":[{"type":"print","value":"2192-6352"},{"type":"electronic","value":"2192-6360"}],"subject":[],"published":{"date-parts":[[2020,10,1]]},"assertion":[{"value":"24 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}