{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:23:34Z","timestamp":1747153414342,"version":"3.40.5"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031741821"},{"type":"electronic","value":"9783031741838"}],"license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-74183-8_28","type":"book-chapter","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T07:04:34Z","timestamp":1728371074000},"page":"339-351","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early Failure Detection for\u00a0Air Production Unit in\u00a0Metro Trains"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3868-6143","authenticated-orcid":false,"given":"Amelia","family":"Zafra","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7980-0972","authenticated-orcid":false,"given":"Bruno","family":"Veloso","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3357-1195","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Gama","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.cie.2017.10.033","volume":"115","author":"M Baptista","year":"2018","unstructured":"Baptista, M., Sankararaman, S., de Medeiros, I.P., Nascimento, C., Jr., Prendinger, H., Henriques, E.M.: Forecasting fault events for predictive maintenance using data-driven techniques and arma modeling. Comput. Ind. Eng. 115, 41\u201353 (2018)","journal-title":"Comput. Ind. Eng."},{"key":"28_CR2","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-3-030-66770-2_5","volume-title":"IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning","author":"M Barros","year":"2020","unstructured":"Barros, M., Veloso, B., Pereira, P.M., Ribeiro, R.P., Gama, J.: Failure detection of an air production unit in operational context. In: Gama, J., Pashami, S., Bifet, A., Sayed-Mouchawe, M., Fr\u00f6ning, H., Pernkopf, F., Schiele, G., Blott, M. (eds.) ITEM\/IoT Streams -2020. CCIS, vol. 1325, pp. 61\u201374. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66770-2_5"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Tong, S., Lu, X., Wei, Y.: A semi-supervised railway foreign object detection method based on GAN. In: Proceedings of the 5th International Conference on Computer Science and Application Engineering, pp.\u00a01\u20135 (2021)","DOI":"10.1145\/3487075.3487133"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Davari, N., Veloso, B., Costa, G.d.A., Pereira, P.M., Ribeiro, R.P., Gama, J.: A survey on data-driven predictive maintenance for the railway industry. Sensors 21(17), 5739 (2021)","DOI":"10.3390\/s21175739"},{"key":"28_CR5","doi-asserted-by":"publisher","DOI":"10.24432\/C5VW3R","author":"N Davari","year":"2023","unstructured":"Davari, N., Veloso, B., Ribeiro, R., Gama, J.: MetroPT-3 Dataset. UCI Mach. Learn. Repository (2023). https:\/\/doi.org\/10.24432\/C5VW3R","journal-title":"UCI Mach. Learn. Repository"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Davari, N., Veloso, B., Ribeiro, R.P., Pereira, P.M., Gama, J.: Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/DSAA53316.2021.9564181"},{"issue":"2","key":"28_CR7","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1108\/JQME-10-2020-0107","volume":"29","author":"D Divya","year":"2023","unstructured":"Divya, D., Marath, B., Santosh Kumar, M.: Review of fault detection techniques for predictive maintenance. J. Qual. Maint. Eng. 29(2), 420\u2013441 (2023)","journal-title":"J. Qual. Maint. Eng."},{"issue":"5","key":"28_CR8","doi-asserted-by":"publisher","first-page":"e1471","DOI":"10.1002\/widm.1471","volume":"12","author":"A Esteban","year":"2022","unstructured":"Esteban, A., Zafra, A., Ventura, S.: Data mining in predictive maintenance systems: a taxonomy and systematic review. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 12(5), e1471 (2022)","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Disc."},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Filios, G., Katsidimas, I., Nikoletseas, S., Panagiotou, S., Raptis, T.P.: An agnostic data-driven approach to predict stoppages of industrial packing machine in near future. In: 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 236\u2013243. IEEE (2020)","DOI":"10.1109\/DCOSS49796.2020.00046"},{"key":"28_CR10","unstructured":"Garc\u00eda, S., Herrera, F.: An extension on \u201cstatistical comparisons of classifiers over multiple data sets\u201d for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677\u20132694 (2009). http:\/\/www.jmlr.org\/papers\/volume9\/garcia08a\/garcia08a.pdf"},{"issue":"2","key":"28_CR11","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1007\/s10462-022-10199-0","volume":"56","author":"Z Hajirahimi","year":"2023","unstructured":"Hajirahimi, Z., Khashei, M.: Hybridization of hybrid structures for time series forecasting: a review. Artif. Intell. Rev. 56(2), 1201\u20131261 (2023)","journal-title":"Artif. Intell. Rev."},{"issue":"8","key":"28_CR12","doi-asserted-by":"publisher","first-page":"2679","DOI":"10.1109\/TIM.2018.2868490","volume":"68","author":"G Kang","year":"2018","unstructured":"Kang, G., Gao, S., Yu, L., Zhang, D.: Deep architecture for high-speed railway insulator surface defect detection: denoising autoencoder with multitask learning. IEEE Trans. Instrum. Meas. 68(8), 2679\u20132690 (2018)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"19","key":"28_CR13","doi-asserted-by":"publisher","first-page":"9290","DOI":"10.3390\/app11199290","volume":"11","author":"J Kang","year":"2021","unstructured":"Kang, J., Kim, C.S., Kang, J.W., Gwak, J.: Anomaly detection of the brake operating unit on metro vehicles using a one-class LSTM autoencoder. Appl. Sci. 11(19), 9290 (2021)","journal-title":"Appl. Sci."},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Kang, S., Sristi, S., Karachiwala, J., Hu, Y.C.: Detection of anomaly in train speed for intelligent railway systems. In: 2018 International Conference on Control, Automation and Diagnosis (ICCAD), pp.\u00a01\u20136. IEEE (2018)","DOI":"10.1109\/CADIAG.2018.8751374"},{"key":"28_CR15","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.eswa.2017.05.079","volume":"87","author":"G Manco","year":"2017","unstructured":"Manco, G., et al.: Fault detection and explanation through big data analysis on sensor streams. Expert Syst. Appl. 87, 141\u2013156 (2017)","journal-title":"Expert Syst. Appl."},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Najjar, A., Ashqar, H.I., Hasasneh, A.: Predictive maintenance of urban metro vehicles: classification of air production unit failures using machine learning. Available at SSRN 4403258 (2023)","DOI":"10.2139\/ssrn.4403258"},{"key":"28_CR17","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.cirpj.2022.11.004","volume":"40","author":"P Nunes","year":"2023","unstructured":"Nunes, P., Santos, J., Rocha, E.: Challenges in predictive maintenance-a review. CIRP J. Manuf. Sci. Technol. 40, 53\u201367 (2023)","journal-title":"CIRP J. Manuf. Sci. Technol."},{"issue":"3","key":"28_CR18","first-page":"829","volume":"10","author":"D Samariya","year":"2023","unstructured":"Samariya, D., Thakkar, A.: A comprehensive survey of anomaly detection algorithms. Ann. Data Sci. 10(3), 829\u2013850 (2023)","journal-title":"Ann. Data Sci."},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Shi, W., Lu, N., Jiang, B., Zhi, Y., Xu, Z.: An unsupervised anomaly detection method based on density peak clustering for rail vehicle door system. In: 2019 Chinese Control and Decision Conference (CCDC), pp. 1954\u20131959. IEEE (2019)","DOI":"10.1109\/CCDC.2019.8833427"},{"issue":"7","key":"28_CR20","doi-asserted-by":"publisher","first-page":"4644","DOI":"10.3390\/app13074644","volume":"13","author":"H Song","year":"2023","unstructured":"Song, H., Choi, H.: Forecasting stock market indices using the recurrent neural network based hybrid models: CNN-LSTM, GRU-CNN, and ensemble models. Appl. Sci. 13(7), 4644 (2023)","journal-title":"Appl. Sci."},{"issue":"1","key":"28_CR21","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1038\/s41597-022-01877-3","volume":"9","author":"B Veloso","year":"2022","unstructured":"Veloso, B., Ribeiro, R.P., Gama, J., Pereira, P.M.: The metropt dataset for predictive maintenance. Sci. Data 9(1), 764 (2022)","journal-title":"Sci. Data"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, W.: Graph based approach to real-time metro passenger flow anomaly detection. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 2744\u20132749. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00318"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-74183-8_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T07:13:13Z","timestamp":1728371593000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74183-8_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,9]]},"ISBN":["9783031741821","9783031741838"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74183-8_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,9]]},"assertion":[{"value":"9 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/haisconference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}