{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T22:35:32Z","timestamp":1770676532937,"version":"3.49.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,7,22]],"date-time":"2016-07-22T00:00:00Z","timestamp":1469145600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Project MAESTRA","award":["ICT-2013-612944"],"award-info":[{"award-number":["ICT-2013-612944"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2016,10]]},"DOI":"10.1007\/s10994-016-5584-6","type":"journal-article","created":{"date-parts":[[2016,7,22]],"date-time":"2016-07-22T16:56:53Z","timestamp":1469206613000},"page":"127-153","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Sequential anomalies: a study in the Railway Industry"],"prefix":"10.1007","volume":"105","author":[{"given":"Rita P.","family":"Ribeiro","sequence":"first","affiliation":[]},{"given":"Pedro","family":"Pereira","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Gama","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,7,22]]},"reference":[{"key":"5584_CR1","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4614-6396-2","volume-title":"Outlier analysis","author":"CC Aggarwal","year":"2013","unstructured":"Aggarwal, C. C. (2013). Outlier analysis. Berlin: Springer."},{"issue":"1","key":"5584_CR2","first-page":"12","volume":"1","author":"C Angeli","year":"2004","unstructured":"Angeli, C., & Chatzinikolaou, A. (2004). On-line fault detection techniques for technical systems: A survey. International Journal of Computer Science & Applications, 1(1), 12\u201330.","journal-title":"International Journal of Computer Science & Applications"},{"issue":"3","key":"5584_CR3","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/0005-1098(88)90073-8","volume":"24","author":"M Basseville","year":"1988","unstructured":"Basseville, M. (1988). Detecting changes in signals and systems a survey. Automatica, 24(3), 309\u2013326.","journal-title":"Automatica"},{"key":"5584_CR4","volume-title":"Detection of abrupt changes: Theory and application","author":"M Basseville","year":"1993","unstructured":"Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application (Vol. 104). Englewood Cliffs: Prentice Hall."},{"key":"5584_CR5","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1145\/335191.335388","volume":"29","author":"MM Breunig","year":"2000","unstructured":"Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). Lof: Identifying density-based local outliers. ACM Sigmod Record, ACM, 29, 93\u2013104.","journal-title":"ACM Sigmod Record, ACM"},{"issue":"3","key":"5584_CR6","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 15.","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"1\u20132","key":"5584_CR7","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0004-3702(96)00034-3","volume":"89","author":"TG Dietterich","year":"1997","unstructured":"Dietterich, T. G., Lathrop, R. H., & Lozano-P\u00e9rez, T. (1997). Solving the multiple instance problem with axis-parallel rectangles. Artif Intell, 89(1\u20132), 31\u201371.","journal-title":"Artif Intell"},{"issue":"1","key":"5584_CR8","first-page":"38","volume":"31","author":"A Duyar","year":"2011","unstructured":"Duyar, A. (2011). Simplifying predictive maintenance. Orbit, 31(1), 38\u201345.","journal-title":"Orbit"},{"key":"5584_CR9","unstructured":"Eugene\u00a0Dubossarsky, Y.T. (2015). Autoencoder: Sparse autoencoder for automatic learning of representative features from unlabeled data. http:\/\/CRAN.R-project.org\/package=autoencoder , r package version 1.1"},{"key":"5584_CR10","unstructured":"European Parliament and Council of the European Union of 7 July 2010 (2010) Directive 2010\/40\/EU on the framework for the deployment of intelligent transport systems in the field of road transport and for interfaces with other modes of transport. Official Journal of the European Union"},{"issue":"9","key":"5584_CR11","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.1109\/TKDE.2013.184","volume":"26","author":"M Gupta","year":"2014","unstructured":"Gupta, M., Gao, J., Aggarwal, C. C., & Han, J. (2014). Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and data Engineering, 26(9), 2250\u20132267.","journal-title":"IEEE Transactions on Knowledge and data Engineering"},{"issue":"1","key":"5584_CR12","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":"5584_CR13","volume-title":"Data Mining: Concepts and Techniques","author":"J Han","year":"2011","unstructured":"Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.","edition":"3"},{"key":"5584_CR14","doi-asserted-by":"crossref","DOI":"10.1007\/978-94-015-3994-4","volume-title":"Identification of outliers","author":"DM Hawkins","year":"1980","unstructured":"Hawkins, D. M. (1980). Identification of outliers (Vol. 11). London: Chapman and Hall."},{"key":"5584_CR15","doi-asserted-by":"crossref","unstructured":"Hempstalk, K., Frank, E., & Witten, I.H. (2008). One-class classification by combining density and class probability estimation. In: Daelemans W, Goethals B, Morik K (eds) Machine Learning and Knowledge Discovery in Databases, European Conference, ECML\/PKDD 2008, Antwerp, Belgium, September 15\u201319, 2008, Proceedings, Part I, Springer, Lecture Notes in Computer Science, vol 5211, pp 505\u2013519","DOI":"10.1007\/978-3-540-87479-9_51"},{"issue":"2","key":"5584_CR16","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s00180-008-0119-7","volume":"24","author":"K Hornik","year":"2009","unstructured":"Hornik, K., Buchta, C., & Zeileis, A. (2009). Open-source machine learning: R meets Weka. Computational Statistics, 24(2), 225\u2013232.","journal-title":"Computational Statistics"},{"key":"5584_CR17","unstructured":"Japkowicz, N., Myers, C., & Gluck, M.A. (1995). A novelty detection approach to classification. InProceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 95, Mo ntr\u00e9al Qu\u00e9bec, Canada, August 20\u201325 1995, 2 Volumes, Morgan Kaufmann, pp 518\u2013523"},{"issue":"1","key":"5584_CR18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/10789669.2005.10391123","volume":"11","author":"S Katipamula","year":"2005","unstructured":"Katipamula, S., & Brambley, M. R. (2005). Methods for fault detection, diagnostics, and prognostics for building systems a review, part i. International Journal of HVAC Research, 11(1), 3\u201325.","journal-title":"International Journal of HVAC Research"},{"key":"5584_CR19","unstructured":"Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2014). e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. http:\/\/CRAN.R-project.org\/package=e1071 , r package version 1.6-4"},{"key":"5584_CR20","doi-asserted-by":"crossref","unstructured":"Nowlan, F., & Heap, H. (1978). Reliability-centered maintenance. Washington, D.C.: Dolby Access Press","DOI":"10.21236\/ADA066579"},{"issue":"4","key":"5584_CR21","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1109\/TNN.2007.899182","volume":"18","author":"A Papadimitropoulos","year":"2007","unstructured":"Papadimitropoulos, A., Rovithakis, G. A., & Parisini, T. (2007). Fault detection in mechanical systems with friction phenomena: An online neural approximation approach. IEEE Transactions on Neural Networks, 18(4), 1067\u20131082.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"5584_CR22","volume-title":"Issues of Fault Diagnosis for Dynamic Systems","author":"RJ Patton","year":"2010","unstructured":"Patton, R. J., Frank, P. M., & Clark, R. N. (2010). Issues of Fault Diagnosis for Dynamic Systems (1st ed.). Berlin: Springer Publishing Company Incorporated.","edition":"1"},{"issue":"3","key":"5584_CR23","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/A:1024099825458","volume":"52","author":"F Provost","year":"2003","unstructured":"Provost, F., & Domingos, P. (2003). Tree induction for probability-based ranking. Machine Learning, 52(3), 199\u2013215.","journal-title":"Machine Learning"},{"key":"5584_CR24","unstructured":"R Core Team (2014) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, http:\/\/www.R-project.org\/"},{"issue":"1","key":"5584_CR25","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1007\/s10618-012-0300-z","volume":"28","author":"E Schubert","year":"2014","unstructured":"Schubert, E., Zimek, A., & Kriegel, H. P. (2014). Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection. Data Mining and Knowledge Discovery, 28(1), 190\u2013237.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"5584_CR26","doi-asserted-by":"crossref","DOI":"10.1002\/0471656372","volume-title":"Introduction to digital signal processing and filter design","author":"BA Shenoi","year":"2005","unstructured":"Shenoi, B. A. (2005). Introduction to digital signal processing and filter design. Hoboken: John Wiley & Sons."},{"key":"5584_CR27","doi-asserted-by":"crossref","unstructured":"Sipos, R., Fradkin, D., Moerchen, F., & Wang, Z. (2014). Log-based predictive maintenance. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, KDD \u201914, pp 1867\u20131876","DOI":"10.1145\/2623330.2623340"},{"key":"5584_CR28","first-page":"303","volume-title":"Proceedings of the 2010 Conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers\u2019 Symposium","author":"B Sluban","year":"2010","unstructured":"Sluban, B., Gamberger, D., & Lavra\u010d, N. (2010). Performance analysis of class noise detection algorithms. Proceedings of the 2010 Conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers\u2019 Symposium (pp. 303\u2013314). Amsterdam, The Netherlands: IOS Press."},{"key":"5584_CR29","unstructured":"Tax, D. (2001). One-class classification: Concept learning in the absence of counter-examples. PhD thesis, Technische Universiteit Delft"},{"issue":"1","key":"5584_CR30","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DMJ Tax","year":"2004","unstructured":"Tax, D. M. J., & Duin, R. P. W. (2004). Support vector data description. Machine Learning, 54(1), 45\u201366.","journal-title":"Machine Learning"},{"key":"5584_CR31","volume-title":"Exploratory Data Analysis","author":"JW Tukey","year":"1977","unstructured":"Tukey, J. W. (1977). Exploratory Data Analysis. Boston: Addison-Wesley."},{"issue":"4","key":"5584_CR32","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/0098-1354(90)87027-M","volume":"14","author":"L Ungar","year":"1990","unstructured":"Ungar, L., Powell, B., & Kamens, S. (1990). Adaptive networks for fault diagnosis and process control. Computers & Chemical Engineering, 14(4), 561\u2013572.","journal-title":"Computers & Chemical Engineering"},{"issue":"1","key":"5584_CR33","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/0952-1976(92)90093-Y","volume":"5","author":"R Vaidyanathan","year":"1992","unstructured":"Vaidyanathan, R., & Venkatasubramanian, V. (1992). Representing and diagnosing dynamic process data using neural networks. Engineering Applications of Artificial Intelligence, 5(1), 11\u201321.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"12","key":"5584_CR34","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1002\/aic.690351210","volume":"35","author":"V Venkatasubramanian","year":"1989","unstructured":"Venkatasubramanian, V., & Chan, K. (1989). A neural network methodology for process fault diagnosis. AIChE Journal, 35(12), 1993\u20132002.","journal-title":"AIChE Journal"},{"issue":"7","key":"5584_CR35","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/0098-1354(90)87081-Y","volume":"14","author":"V Venkatasubramanian","year":"1990","unstructured":"Venkatasubramanian, V., Vaidyanathan, R., & Yamamoto, Y. (1990). Process fault detection and diagnosis using neural networksi. Steady-state processes. Computers & Chemical Engineering, 14(7), 699\u2013712.","journal-title":"Computers & Chemical Engineering"},{"issue":"11","key":"5584_CR36","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1002\/aic.690351106","volume":"35","author":"K Watanabe","year":"1989","unstructured":"Watanabe, K., Matsuura, I., Abe, M., Kubota, M., & Himmelblau, D. (1989). Incipient fault diagnosis of chemical processes via artificial neural networks. AIChE Journal, 35(11), 1803\u20131812.","journal-title":"AIChE Journal"},{"issue":"5","key":"5584_CR37","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1002\/aic.690400510","volume":"40","author":"K Watanabe","year":"1994","unstructured":"Watanabe, K., Hirota, S., Hou, L., & Himmelblau, D. (1994). Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks. AIChE Journal, 40(5), 839\u2013848.","journal-title":"AIChE Journal"},{"key":"5584_CR38","doi-asserted-by":"crossref","unstructured":"Yilboga, H., Eker, OF., Guculu, A., & Camci, F. (2010). Failure prediction on railway turnouts using time delay neural networks. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, pp 134\u2013137","DOI":"10.1109\/CIMSA.2010.5611756"},{"key":"5584_CR39","first-page":"082","volume-title":"Intelligent computing in signal processing and pattern recognition, lecture notes in control and information sciences","author":"J Zhang","year":"2006","unstructured":"Zhang, J., Yan, Q., Zhang, Y., & Huang, Z. (2006). Novel fault class detection based on novelty detection methods. In D. Huang, K. Li, & G. Irwin (Eds.), Intelligent computing in signal processing and pattern recognition, lecture notes in control and information sciences (pp. 082\u2013987). Berlin: Springer."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-016-5584-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-016-5584-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-016-5584-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T16:31:57Z","timestamp":1568219517000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-016-5584-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,22]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2016,10]]}},"alternative-id":["5584"],"URL":"https:\/\/doi.org\/10.1007\/s10994-016-5584-6","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7,22]]}}}