{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T00:07:19Z","timestamp":1763165239859,"version":"3.37.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030356521"},{"type":"electronic","value":"9783030356538"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-35653-8_3","type":"book-chapter","created":{"date-parts":[[2019,11,22]],"date-time":"2019-11-22T08:02:59Z","timestamp":1574409779000},"page":"33-44","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine Learning Based Monitoring of the Pneumatic Actuators\u2019 Behavior Through Signal Processing Using Real-World Data Set"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7408-998X","authenticated-orcid":false,"given":"Tibor","family":"Kov\u00e1cs","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0023-1143","authenticated-orcid":false,"given":"Andrea","family":"K\u0151","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,20]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.jmsy.2018.01.006","volume":"48","author":"F Tao","year":"2018","unstructured":"Tao, F., Qi, Q., Liu, A., Kusiak, A.: Data-driven smart manufacturing. J. Manuf. Syst. 48, 157\u2013169 (2018). \nhttps:\/\/doi.org\/10.1016\/j.jmsy.2018.01.006","journal-title":"J. Manuf. Syst."},{"key":"3_CR2","doi-asserted-by":"publisher","unstructured":"Helwig, N., Pignanelli, E., Sch\u00fctze, A.: Detecting and compensating sensor faults in a hydraulic condition monitoring system. In: Proceedings Sensor, pp. 641\u2013646 (2015). \nhttps:\/\/doi.org\/10.5162\/sensor2015\/D8.1","DOI":"10.5162\/sensor2015\/D8.1"},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1754414.1754419","volume":"6","author":"AB Sharma","year":"2010","unstructured":"Sharma, A.B., Golubchik, L., Govindan, R.: Sensor faults: detection methods and prevalence in real-world datasets. ACM Trans. Sens. Netw. 6, 1\u201339 (2010). \nhttps:\/\/doi.org\/10.1145\/1754414.1754419","journal-title":"ACM Trans. Sens. Netw."},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/JPROC.2015.2388958","volume":"103","author":"S Yin","year":"2015","unstructured":"Yin, S., Kaynak, O.: Big data for modern industry: challenges and trends. Proc. IEEE 103, 143\u2013146 (2015). \nhttps:\/\/doi.org\/10.1109\/JPROC.2015.2388958","journal-title":"Proc. IEEE"},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-015-0034-z","volume":"2","author":"P O\u2019Donovan","year":"2015","unstructured":"O\u2019Donovan, P., Leahy, K., Bruton, K., O\u2019Sullivan, D.T.J.: An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. J. Big Data 2, 1\u201326 (2015). \nhttps:\/\/doi.org\/10.1186\/s40537-015-0034-z","journal-title":"J. Big Data"},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.ijinfomgt.2014.10.007","volume":"35","author":"A Gandomi","year":"2015","unstructured":"Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manage. 35, 137\u2013144 (2015). \nhttps:\/\/doi.org\/10.1016\/j.ijinfomgt.2014.10.007","journal-title":"Int. J. Inf. Manage."},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1243\/095440505X32274","volume":"219","author":"DT Pham","year":"2005","unstructured":"Pham, D.T., Afify, A.A.: Machine-learning techniques and their applications in manufacturing. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 219, 395\u2013412 (2005). \nhttps:\/\/doi.org\/10.1243\/095440505X32274","journal-title":"Proc. Inst. Mech. Eng. Part B J. Eng. Manuf."},{"key":"3_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-4018-5","volume-title":"Missing Data","author":"JW Graham","year":"2012","unstructured":"Graham, J.W.: Missing Data. Springer, New York (2012). \nhttps:\/\/doi.org\/10.1007\/978-1-4614-4018-5"},{"key":"3_CR9","unstructured":"Kabacoff, R.I.: Advanced methods for missing data. In: R in Action Data Analysis and Graphics with R. Manning Publications Co., p. 472 (2011)"},{"key":"3_CR10","unstructured":"Piddington, C., Pegram, M.: An IMS test case - global manufacturing. In: Proceedings of the IFIP TC5\/WG5.7 Fifth International Conference on Advances in Production Management Systems. North-Holland Publishing Co., Amsterdam, The Netherlands, pp. 11\u201320 (1993)"},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1147\/rd.116.0601","volume":"11","author":"AL Samuel","year":"1967","unstructured":"Samuel, A.L.: Some studies in machine learning using the game of checkers. II\u2014recent progress. IBM J. Res. Dev. 11, 601\u2013617 (1967). \nhttps:\/\/doi.org\/10.1147\/rd.116.0601","journal-title":"IBM J. Res. Dev."},{"key":"3_CR12","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/21693277.2016.1192517","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest, T., Weimer, D., Irgens, C., Thoben, K.-D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4, 23\u201345 (2016). \nhttps:\/\/doi.org\/10.1080\/21693277.2016.1192517","journal-title":"Prod. Manuf. Res."},{"key":"3_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30164-8","volume-title":"Encyclopedia of Machine Learning","author":"C Sammut","year":"2010","unstructured":"Sammut, C., Webb, G.I.: Encyclopedia of Machine Learning. Springer, Boston (2010). \nhttps:\/\/doi.org\/10.1007\/978-0-387-30164-8"},{"key":"3_CR14","unstructured":"Schwabacher, M., Goebel, K.: A survey of artificial intelligence for prognostics. In: AAAI Fall Symposium, pp. 107\u2013114 (2007)"},{"key":"3_CR15","unstructured":"Byington, C.S., Watson, M., Edwards, D., Dunkin, B.: In-line health monitoring system for hydraulic pumps and motors. In: 2003 IEEE Aerospace Conference Proceedings (Cat. No. 03TH8652). IEEE, pp. 3279\u20133287 (2003)"},{"key":"3_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5923\/j.ajis.20160601.01","volume":"6","author":"K Hansson","year":"2016","unstructured":"Hansson, K., Yella, S., Dougherty, M., Fleyeh, H.: Machine learning algorithms in heavy process manufacturing. Am. J. Intell. Syst. 6, 1\u201313 (2016). \nhttps:\/\/doi.org\/10.5923\/j.ajis.20160601.01","journal-title":"Am. J. Intell. Syst."},{"key":"3_CR17","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","volume":"409\u2013410","author":"W-C Lin","year":"2017","unstructured":"Lin, W.-C., Tsai, C.-F., Hu, Y.-H., Jhang, J.-S.: Clustering-based undersampling in class-imbalanced data. Inf. Sci. 409\u2013410, 17\u201326 (2017). \nhttps:\/\/doi.org\/10.1016\/j.ins.2017.05.008","journal-title":"Inf. Sci."},{"key":"3_CR18","doi-asserted-by":"publisher","first-page":"3137","DOI":"10.1109\/TIE.2016.2519325","volume":"63","author":"Y Lei","year":"2016","unstructured":"Lei, Y., Jia, F., Lin, J., et al.: An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans. Industr. Electron. 63, 3137\u20133147 (2016)","journal-title":"IEEE Trans. Industr. Electron."},{"key":"3_CR19","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.ymssp.2010.07.013","volume":"25","author":"K Worden","year":"2011","unstructured":"Worden, K., Staszewski, W.J., Hensman, J.J.: Natural computing for mechanical systems research: a tutorial overview. Mech. Syst. Signal Process. 25, 4\u2013111 (2011)","journal-title":"Mech. Syst. Signal Process."},{"key":"3_CR20","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1109\/TIE.2013.2261033","volume":"61","author":"Y Shatnawi","year":"2013","unstructured":"Shatnawi, Y., Al-Khassaweneh, M.: Fault diagnosis in internal combustion engines using extension neural network. IEEE Trans. Industr. Electron. 61, 1434\u20131443 (2013)","journal-title":"IEEE Trans. Industr. Electron."},{"key":"3_CR21","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1109\/TIE.2014.2319216","volume":"62","author":"D You","year":"2014","unstructured":"You, D., Gao, X., Katayama, S.: WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM. IEEE Trans. Industr. Electron. 62, 628\u2013636 (2014)","journal-title":"IEEE Trans. Industr. Electron."},{"key":"3_CR22","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.ymssp.2007.07.013","volume":"22","author":"Y Lei","year":"2008","unstructured":"Lei, Y., He, Z., Zi, Y., Chen, X.: New clustering algorithm-based fault diagnosis using compensation distance evaluation technique. Mech. Syst. Signal Process. 22, 419\u2013435 (2008)","journal-title":"Mech. Syst. Signal Process."},{"key":"3_CR23","doi-asserted-by":"publisher","first-page":"6418","DOI":"10.1109\/TIE.2014.2301773","volume":"61","author":"S Yin","year":"2014","unstructured":"Yin, S., Ding, S.X., Xie, X., Luo, H.: A review on basic data-driven approaches for industrial process monitoring. IEEE Trans. Industr. Electron. 61, 6418\u20136428 (2014)","journal-title":"IEEE Trans. Industr. Electron."},{"key":"3_CR24","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1098\/rsta.2006.1929","volume":"365","author":"SD Fassois","year":"2007","unstructured":"Fassois, S.D., Sakellariou, J.S.: Time-series methods for fault detection and identification in vibrating structures. Philos. Trans. R. Soc. Lond. Ser. A. Math. Phys. Eng. Sciences 365, 411\u2013448 (2007). \nhttps:\/\/doi.org\/10.1098\/rsta.2006.1929","journal-title":"Philos. Trans. R. Soc. Lond. Ser. A. Math. Phys. Eng. Sciences"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Munirathinam, S., Ramadoss, B.: Big data predictive analtyics for proactive semiconductor equipment maintenance. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 893\u2013902 (2014)","DOI":"10.1109\/BigData.2014.7004320"},{"key":"3_CR26","first-page":"470","volume":"11","author":"MAS Al Tobi","year":"2017","unstructured":"Al Tobi, M.A.S., Bevan, G., Ramachandran, K.P., et al.: Experimental set-up for investigation of fault diagnosis of a centrifugal pump. World Acad. Sci. Eng. Technol. Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng. 11, 470\u2013474 (2017)","journal-title":"World Acad. Sci. Eng. Technol. Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng."},{"key":"3_CR27","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1006\/mssp.1994.1039","volume":"8","author":"O Bardou","year":"1994","unstructured":"Bardou, O., Sidahmed, M.: Early detection of leakages in the exhaust and discharge systems of reciprocating machines by vibration analysis. Mech. Syst. Signal Process. 8, 551\u2013570 (1994). \nhttps:\/\/doi.org\/10.1006\/mssp.1994.1039","journal-title":"Mech. Syst. Signal Process."},{"key":"3_CR28","unstructured":"R Development Core Team R: A Language and Environment for Statistical Computing (2008)"},{"key":"3_CR29","unstructured":"Ulrich, J.M., Ryan, J.A., Bennett, R., Joy, C.: R package \u2018xts\u2019 (2018)"},{"key":"3_CR30","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland, R.B., Cleveland, W.S., McRae, J.E., Terpenning, I.: STL: a seasonal-trend decomposition procedure based on loess. J. Official Stat. 6, 3\u201373 (1990)","journal-title":"J. Official Stat."},{"key":"3_CR31","doi-asserted-by":"publisher","unstructured":"M\u00fcllner, D.: Fastcluster: fast hierarchical, agglomerative clustering routines for R and Python. J. Stat. Softw. 53 (2015). \nhttps:\/\/doi.org\/10.18637\/jss.v053.i09","DOI":"10.18637\/jss.v053.i09"},{"key":"3_CR32","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","volume":"58","author":"JHJ Ward","year":"1963","unstructured":"Ward, J.H.J.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236\u2013244 (1963). \nhttps:\/\/doi.org\/10.1080\/01621459.1963.10500845","journal-title":"J. Am. Stat. Assoc."},{"key":"3_CR33","unstructured":"Calaway, R., Tenenbaum, D., Microsoft, Weston S.: R package: doParallel (2018). \nhttps:\/\/cran.r-project.org\/package=doParallel"},{"key":"3_CR34","unstructured":"Calaway, R., Microsoft, Weston S.: R package: foreach (2017). \nhttps:\/\/cran.r-project.org\/package=foreach"},{"key":"3_CR35","unstructured":"Yau, C.: R package: rpud GPU computation in R (2018)"},{"key":"3_CR36","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1002\/9781118646106.ch2","volume-title":"Imbalanced Learning","author":"Gary M. Weiss","year":"2013","unstructured":"Weiss, G.M.: Imbalanced Learning: Foundations, Algorithms, and Applications: Foundations of Imbalanced Learning, pp. 13\u201341 (2012)"},{"key":"3_CR37","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique nitesh. J. Artif. Intell. Res. 16, 321\u2013357 (2002). \nhttps:\/\/doi.org\/10.1613\/jair.953","journal-title":"J. Artif. Intell. Res."},{"key":"3_CR38","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S. ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322\u20131328 (2008)"}],"container-title":["Lecture Notes in Computer Science","Future Data and Security Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-35653-8_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,22]],"date-time":"2019-11-22T08:06:32Z","timestamp":1574409992000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-35653-8_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030356521","9783030356538"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-35653-8_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"20 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nha Trang City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/fdse.hcmut.edu.vn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}