{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T04:05:16Z","timestamp":1754193916322,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811632631"},{"type":"electronic","value":"9789811632648"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-3264-8_22","type":"book-chapter","created":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T09:33:55Z","timestamp":1622194435000},"page":"232-242","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Rotating Machinery Condition Monitoring Using Time Series Analysis of Vibration Signal"],"prefix":"10.1007","author":[{"given":"Abdellah","family":"Chehri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alfred","family":"Zimmermann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wend-Benedo","family":"Zoungrana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hassan","family":"Ezzaidi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,29]]},"reference":[{"key":"22_CR1","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/978-981-13-8566-7_47","volume-title":"Innovation in Medicine and Healthcare Systems, and Multimedia","author":"A Chehri","year":"2019","unstructured":"Chehri, A., Jeon, G.: The industrial internet of things: examining how the IIoT will improve the predictive maintenance. In: Chen, Y.-W., Zimmermann, A., Howlett, R.J., Jain, L.C. (eds.) Innovation in Medicine and Healthcare Systems, and Multimedia. SIST, vol. 145, pp. 517\u2013527. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-8566-7_47"},{"key":"22_CR2","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/978-981-13-8566-7_46","volume-title":"Innovation in Medicine and Healthcare Systems, and Multimedia","author":"A Chehri","year":"2019","unstructured":"Chehri, A., Jeon, G.: Routing protocol in the industrial internet of things for smart factory monitoring. In: Chen, Y.-W., Zimmermann, A., Howlett, R.J., Jain, L.C. (eds.) Innovation in Medicine and Healthcare Systems, and Multimedia. SIST, vol. 145, pp. 505\u2013515. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-8566-7_46"},{"issue":"7","key":"22_CR3","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. Sig. Process. 20(7), 1483\u20131510 (2006)","journal-title":"Mech. Syst. Sig. Process."},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.1002\/we.1801","volume":"18","author":"J Igba","year":"2015","unstructured":"Igba, J., Alemzadeh, K., Henningsen, K., Durugbo, C.: Effect of preventive maintenance intervals on reliability and maintenance costs of wind turbine gearboxes. Wind Energ. 18, 2013\u20132024 (2015)","journal-title":"Wind Energ."},{"key":"22_CR5","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.engappai.2014.09.006","volume":"37","author":"G Medina-Oliva","year":"2015","unstructured":"Medina-Oliva, G., Weber, P., Iung, B.: Industrial system knowledge formalization to aid decision making in maintenance strategies assessment. Eng. Appl. Artif. Intell. 37, 343\u2013360 (2015)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"22_CR6","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/S0963-8695(01)00063-9","volume":"35","author":"B Liu","year":"2002","unstructured":"Liu, B., Ling, S.F., Gribonval, R.: Bearing failure detection using matching pursuit. NDT and E Int. 35(4), 255\u2013262 (2002)","journal-title":"NDT and E Int."},{"issue":"1","key":"22_CR7","first-page":"63","volume":"12","author":"ZF Ninoslav","year":"2015","unstructured":"Ninoslav, Z.F., Rusmir, B., Cvetkovic, D.: Vibration feature extraction methods for gear faults diagnosis\u2014a review. Facta Universitatis Ser.: Working Living Environ. Protection 12(1), 63\u201372 (2015)","journal-title":"Facta Universitatis Ser.: Working Living Environ. Protection"},{"issue":"1","key":"22_CR8","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1016\/j.ymssp.2011.08.002","volume":"27","author":"GF Bin","year":"2012","unstructured":"Bin, G.F., Gao, J.J., Li, X.J., Dhillon, B.S.: Early fault diagnosis of rotating machinery based on wavelet packets\u2014empirical mode decomposition feature extraction and neural network. Mech. Syst. Sig. Process. 27(1), 696\u2013711 (2012)","journal-title":"Mech. Syst. Sig. Process."},{"key":"22_CR9","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-981-15-5784-2_16","volume-title":"Human Centred Intelligent Systems","author":"W-B Zoungrana","year":"2021","unstructured":"Zoungrana, W.-B., Chehri, A., Zimmermann, A.: Automatic classification of rotating machinery defects using machine learning (ML) algorithms. In: Zimmermann, A., Howlett, R.J., Jain, L.C. (eds.) Human Centred Intelligent Systems. SIST, vol. 189, pp. 193\u2013203. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-5784-2_16"},{"unstructured":"Lee, J., Qiu, H., Yu, G., Lin, J.: Rexnord Technical Services, IMS, University of Cincinnati. \u201cBearing Data Set\u201d NASA Ames Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA","key":"22_CR10"},{"doi-asserted-by":"crossref","unstructured":"Zurita-Mill\u00e1n, D., et al.: Vibration signal forecasting on rotating machinery by means of signal decomposition and neurofuzzy modelling. Shock Vibr. 2016 (2016)","key":"22_CR11","DOI":"10.1155\/2016\/2683269"},{"issue":"8","key":"22_CR12","doi-asserted-by":"publisher","first-page":"2200","DOI":"10.1109\/TIM.2012.2184015","volume":"61","author":"J Yu","year":"2012","unstructured":"Yu, J.: Health condition monitoring of machines based on hidden Markov model and contribution analysis. IEEE Trans. Inst. Meas. 61(8), 2200\u20132211 (2012)","journal-title":"IEEE Trans. Inst. Meas."},{"issue":"3","key":"22_CR13","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/TIM.2010.2078296","volume":"60","author":"A Malhi","year":"2011","unstructured":"Malhi, A., Yan, R., Gao, R.X.: Prognosis of defect propagation based on recurrent neural networks. IEEE Trans. Instrum. Meas. 60(3), 703\u2013711 (2011)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"22_CR14","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.microrel.2017.03.006","volume":"75","author":"Z Chen","year":"2017","unstructured":"Chen, Z., Deng, S., Chen, X., Li, C., Sanchez, R.V., Qin, H.: Deep neural network-based rolling bearing fault diagnosis. Microelectron Reliab. 75, 327\u2013333 (2017)","journal-title":"Microelectron Reliab."},{"unstructured":"Peeters, C., Guillaume, P., Helsen, J.: Vibration data pre-processing techniques for rolling element bearing fault detection. In: International Conference on Sound and Vibration (2016)","key":"22_CR15"},{"unstructured":"McCormic, R., Hartmann, D.: Smart Factories Need Smart Machines. Analog Devices Technical Article (2015)","key":"22_CR16"},{"issue":"2","key":"22_CR17","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TR.2012.2194177","volume":"61","author":"DA Tobon-Mejia","year":"2012","unstructured":"Tobon-Mejia, D.A., Medjaher, K., Zerhouni, N., Tripot, G.: A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. IEEE Trans. Reliab. 61(2), 491\u2013503 (2012)","journal-title":"IEEE Trans. Reliab."},{"doi-asserted-by":"crossref","unstructured":"Wang, F., Zhang, Y., Zhang, B., Su, W.: Application of wavelet packet sample entropy in the forecast of rolling element bearing fault trend. In: Proceedings of the 2011 International Conference on Multimedia and Signal Processing, vol. 02, pp. 12\u201316 (2011)","key":"22_CR18","DOI":"10.1109\/CMSP.2011.93"},{"doi-asserted-by":"crossref","unstructured":"Mortada, M., Yacout, S.: cbmLAD - using logical analysis of data in condition based maintenance. Inter.  Conf. Com. Res. Dev. 4, 30\u201334 (2011)","key":"22_CR19","DOI":"10.1109\/ICCRD.2011.5763847"},{"doi-asserted-by":"crossref","unstructured":"Yang, W., Court, R., Jiang, I.: Wind turbine condition monitoring by the approach of SCADA data analysis. Renew. Energy 53 (2013)","key":"22_CR20","DOI":"10.1016\/j.renene.2012.11.030"},{"doi-asserted-by":"crossref","unstructured":"Guo, P., Bai, N.: Wind turbine gearbox condition monitoring with AAKR and moving window statistic methods. Energies 4 (2011)","key":"22_CR21","DOI":"10.3390\/en4112077"},{"doi-asserted-by":"crossref","unstructured":"Saber, M., Saadane, R., Aroussi, H., Chehri, A.: An optimized spectrum sensing implementation based on SVM, KNN and TREE algorithms. In: International Conference on Signal Image Technology & Internet Based Systems, Sorrento (NA), Italie, November 2019","key":"22_CR22","DOI":"10.1109\/SITIS.2019.00068"},{"unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification andRegression Trees. Wadsworth (1984). ISBN 0\u2013534\u201398053\u20138","key":"22_CR23"},{"unstructured":"Yamada, Y., Suzuki, E., Yokoi, H., Takabayashi, K.: Decision-tree induction from time-series data based on a standard-example splittest. In: Machine Learning, Proceedings of the Twentieth International Conference, 21\u201324 August, Washington, DC, USA (2003)","key":"22_CR24"},{"unstructured":"Kilian, Q., et al.: Distance metric learning for large margin nearest neighbor classification. In: Advances in Neural Information Processing Systems, pp. 1473\u20131480 (2006)","key":"22_CR25"},{"doi-asserted-by":"crossref","unstructured":"Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29, 131\u2013163 (1997)","key":"22_CR26","DOI":"10.1023\/A:1007465528199"}],"container-title":["Smart Innovation, Systems and Technologies","Human Centred Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-3264-8_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T09:40:52Z","timestamp":1622194852000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-3264-8_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811632631","9789811632648"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-3264-8_22","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"type":"print","value":"2190-3018"},{"type":"electronic","value":"2190-3026"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"29 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KES-HCIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Centered Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"keshcis2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/hcis-21.kesinternational.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}