{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:04:07Z","timestamp":1766268247107},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2014,11,21]],"date-time":"2014-11-21T00:00:00Z","timestamp":1416528000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2017,2]]},"DOI":"10.1007\/s10845-014-1000-x","type":"journal-article","created":{"date-parts":[[2014,11,21]],"date-time":"2014-11-21T16:45:00Z","timestamp":1416588300000},"page":"489-500","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Detection of defective embedded bearings by sound analysis: a machine learning approach"],"prefix":"10.1007","volume":"28","author":[{"given":"Mario A.","family":"Saucedo-Espinosa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hugo Jair","family":"Escalante","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arturo","family":"Berrones","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2014,11,21]]},"reference":[{"key":"1000_CR1","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/s00170-007-1330-3","volume":"40","author":"KF Al-Raheem","year":"2009","unstructured":"Al-Raheem, K. F., Roy, A., Ramachandran, K. P., Harrison, D. K., & Grainger, S. (2009). Rolling element bearing faults diagnosis based on autocorrelation of optimized: Wavelet de-noising technique. The International Journal of Advanced Manufacturing Technology, 40, 393\u2013402.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1000_CR2","unstructured":"Benkedjouh, T,, Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, 1\u201311."},{"key":"1000_CR3","volume-title":"Pattern recognition and machine learning","author":"C Bishop","year":"2007","unstructured":"Bishop, C. (2007). Pattern recognition and machine learning (1st ed.). Berlin: Springer.","edition":"1"},{"key":"1000_CR4","doi-asserted-by":"crossref","unstructured":"Dey, D., Solorio, T., Montes, M., & Escalante, H. J. (2011). Instance selection in text classification using the silhouette coefficient measure. Lecture Notes in Computer Science, vol. 7094, pp. 357\u2013369.","DOI":"10.1007\/978-3-642-25324-9_31"},{"key":"1000_CR5","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"T Golub","year":"1999","unstructured":"Golub, T., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., et al. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531\u2013537.","journal-title":"Science"},{"issue":"3","key":"1000_CR6","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1109\/TIM.2002.1017721","volume":"51","author":"S Goumas","year":"2002","unstructured":"Goumas, S., Zervakis, M. E., & Stavrakakis, G. S. (2002). Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction. IEEE Transactions on Instrumentation and Measurement, 51(3), 497\u2013508.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"1000_CR7","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157\u20131182.","journal-title":"The Journal of Machine Learning Research"},{"key":"1000_CR8","volume-title":"Feature extraction, foundations and applications, studies in fuzziness and soft computing","year":"2006","unstructured":"Guyon, I., Gunn, S., Nikravesh, M., & Zadej, L. A. (Eds.). (2006). Feature extraction, foundations and applications, studies in fuzziness and soft computing (Vol. 207). Berlin: Springer."},{"issue":"1","key":"1000_CR9","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. SIGKDD Explorations, 11(1), 10\u201318.","journal-title":"SIGKDD Explorations"},{"key":"1000_CR10","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1016\/j.eswa.2010.07.119","volume":"38","author":"P Kankar","year":"2011","unstructured":"Kankar, P., Sharma, S., & Harsha, S. (2011a). Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 38, 1876\u20131886.","journal-title":"Expert Systems with Applications"},{"key":"1000_CR11","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1016\/j.neucom.2011.01.021","volume":"74","author":"P Kankar","year":"2011","unstructured":"Kankar, P., Sharma, S., & Harsha, S. (2011b). Rolling element bearing fault diagnosis using wavelet transform. Neurocomputing, 74, 1638\u20131645.","journal-title":"Neurocomputing"},{"key":"1000_CR12","doi-asserted-by":"crossref","unstructured":"Li, H., Lian, X., Guo, C., & Zhao, P. (2013). Investigation on early fault classification for rolling element bearing based on the optimal frequency band determination. Journal of Intelligent Manufacturing, 1\u201310","DOI":"10.1007\/s00170-012-4372-0"},{"key":"1000_CR13","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s10845-009-0353-z","volume":"23","author":"R Li","year":"2012","unstructured":"Li, R., Sopon, P., & He, D. (2012). Fault features extraction for bearing prognostics. Journal of Intelligent Manufacturing, 23, 313\u2013321.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"2","key":"1000_CR14","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1080\/10402009908982232","volume":"42","author":"Y Li","year":"1999","unstructured":"Li, Y., Billington, S., Zhang, C., Kurfess, T., Danyluk, S., & Liang, S. (1999). Dynamic prognostic prediction of defect propagation on rolling element bearings. Tribology transactions, 42(2), 385\u2013392.","journal-title":"Tribology transactions"},{"issue":"1","key":"1000_CR15","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/0022-460X(84)90595-9","volume":"96","author":"P McFadden","year":"1984","unstructured":"McFadden, P., & Smith, J. (1984). The vibration produced by a single point defect in a rolling element bearing. Journal of Sound and Vibration, 96(1), 69\u201382.","journal-title":"Journal of Sound and Vibration"},{"issue":"1","key":"1000_CR16","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/TE.2002.808234","volume":"46","author":"SA McInerny","year":"2003","unstructured":"McInerny, S. A., & Dai, Y. (2003). Basic vibration signal processing for bearing fault detection. IEEE Transactions on Education, 46(1), 149\u2013156.","journal-title":"IEEE Transactions on Education"},{"key":"1000_CR17","doi-asserted-by":"crossref","unstructured":"Mendel, E., Mariano, L., Drago, I., Loureiro, S., Rauber, T., Varejao, F., & Batista, R. (2008). Automatic bearing fault pattern recognition using vibration signal analysis. In Proceedings of the 2008 IEEE international symposium on industrial electronics. IEEE, pp. 955\u2013960.","DOI":"10.1109\/ISIE.2008.4677026"},{"key":"1000_CR18","volume-title":"Machine learning","author":"TM Mitchell","year":"1997","unstructured":"Mitchell, T. M. (1997). Machine learning (1st ed.). NY: McGraw-Hill Science\/Engineering\/Math.","edition":"1"},{"key":"1000_CR19","volume-title":"Introduction to statistical quality control","author":"DC Montgomery","year":"2007","unstructured":"Montgomery, D. C. (2007). Introduction to statistical quality control. New York: Wiley."},{"issue":"2","key":"1000_CR20","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/BF00572400","volume":"5","author":"BA Osyk","year":"1994","unstructured":"Osyk, B. A., Hung, M. S., & Madey, G. R. (1994). A neural network model for fault detection in conjunction with a programmable logic controller. Journal of Intelligent Manufacturing, 5(2), 67\u201378.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1000_CR21","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/S0263-2241(99)00007-X","volume":"25","author":"N Paonea","year":"1999","unstructured":"Paonea, N., Scalisea, L., Stavrakakisb, G., & Pouliezosc, A. (1999). Fault detection for quality control of household appliances by non-invasive laser doppler technique and likelihood classifier. Measurement, 25, 237\u2013247.","journal-title":"Measurement"},{"key":"1000_CR22","volume-title":"Numerical recipes: The art of scientific computing","author":"WH Press","year":"2007","unstructured":"Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical recipes: The art of scientific computing (3rd ed.). Cambridge: Cambridge University Press.","edition":"3"},{"key":"1000_CR23","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1155\/S1110865704310085","volume":"2004","author":"B Samanta","year":"2004","unstructured":"Samanta, B., Al Balushi, K. R., & Al Araimi, S. A. (2004). Bearing fault detection using artificial neural networks and genetic algorithm. EURASIP Journal on Applied Signal Processing, 2004, 366\u2013377.","journal-title":"EURASIP Journal on Applied Signal Processing"},{"key":"1000_CR24","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s10845-008-0097-1","volume":"19","author":"VS Sharma","year":"2008","unstructured":"Sharma, V. S., Dhiman, S., Sehgal, R., & Sharma, S. K. (2008). Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing, 19, 473\u2013483.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1000_CR25","doi-asserted-by":"crossref","unstructured":"Su, H., Chong, K. T., & Parlos, A. G. (2005). A neural network method for induction machine fault detection with vibration signal. In Proceedings of the 2005 international conference on computational science and its applications, LNCS, vol. 3481, pp. 1293\u20131302.","DOI":"10.1007\/11424826_137"},{"key":"1000_CR26","doi-asserted-by":"crossref","first-page":"4901","DOI":"10.1016\/j.eswa.2010.09.089","volume":"38","author":"V Sugumaran","year":"2011","unstructured":"Sugumaran, V., & Ramachandran, K. (2011). Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning. Expert Systems with Applications, 38, 4901\u20134907.","journal-title":"Expert Systems with Applications"},{"issue":"8","key":"1000_CR27","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/S0301-679X(99)00077-8","volume":"32","author":"N Tandon","year":"1999","unstructured":"Tandon, N., & Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32(8), 469\u2013480.","journal-title":"Tribology International"},{"key":"1000_CR28","volume-title":"Stochastic processes in physics and chemistry","author":"NG Kampen Van","year":"1981","unstructured":"Van Kampen, N. G. (1981). Stochastic processes in physics and chemistry. Amsterdam: North-Holland."},{"key":"1000_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Y., & Tseng, M. (2013). A nave Bayes approach to map customer requirements to product variants. Journal of Intelligent Manufacturing, 1\u20139.","DOI":"10.1016\/j.ijmachtools.2013.05.007"},{"key":"1000_CR30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","volume":"14","author":"X Wu","year":"2008","unstructured":"Wu, X., Kumar, V., Quinlan, J. R., Yang, J. G. Q., McLachlan, H. M. G. J., Ng, A., et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14, 1\u201337.","journal-title":"Knowledge and Information Systems"},{"key":"1000_CR31","doi-asserted-by":"crossref","unstructured":"Wuest, T., Irgens, C., & Thoben, K. D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25(5), 1167\u20131180.","DOI":"10.1007\/s10845-013-0761-y"},{"key":"1000_CR32","doi-asserted-by":"crossref","unstructured":"Yan, R., Gao, R. X., & Chen, X. (2014). Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 96, 1\u201315.","DOI":"10.1016\/j.sigpro.2013.04.015"},{"key":"1000_CR33","doi-asserted-by":"crossref","first-page":"11,311","DOI":"10.1016\/j.eswa.2011.02.181","volume":"38","author":"Y Yang","year":"2011","unstructured":"Yang, Y., Liao, Y., Meng, G., & Lee, J. (2011). A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis. Expert Systems with Applications, 38, 11,311\u201311,320.","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"1000_CR34","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1007\/s10845-012-0657-2","volume":"24","author":"Z Zhang","year":"2013","unstructured":"Zhang, Z., Wang, Y., & Wang, K. (2013). Fault diagnosis and prognosis using wavelet packet decomposition, fourier transform and artificial neural network. Journal of Intelligent Manufacturing, 24(6), 1213\u20131227.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1000_CR35","doi-asserted-by":"crossref","first-page":"10,199","DOI":"10.1016\/j.eswa.2011.02.078","volume":"38","author":"X Zhao","year":"2011","unstructured":"Zhao, X., Li, M., Xu, J., & Song, G. (2011). An effective procedure exploiting unlabeled data to build monitoring system. Expert Systems with Applications, 38, 10,199\u201310,204.","journal-title":"Expert Systems with Applications"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-014-1000-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10845-014-1000-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-014-1000-x","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-014-1000-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,31]],"date-time":"2019-05-31T02:12:04Z","timestamp":1559268724000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10845-014-1000-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,11,21]]},"references-count":35,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,2]]}},"alternative-id":["1000"],"URL":"https:\/\/doi.org\/10.1007\/s10845-014-1000-x","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,11,21]]}}}