{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T21:20:42Z","timestamp":1758057642178,"version":"3.44.0"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051783","type":"print"},{"value":"9783032051790","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-05179-0_18","type":"book-chapter","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T21:43:33Z","timestamp":1757972613000},"page":"232-245","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable Predictive Maintenance: Combining Anomaly Detection with\u00a0Quantitative Root Cause Analysis"],"prefix":"10.1007","author":[{"given":"In\u00eas","family":"Barbosa","sequence":"first","affiliation":[]},{"given":"Jo\u00e3o","family":"Gama","sequence":"additional","affiliation":[]},{"given":"Bruno","family":"Veloso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","unstructured":"Carpentier, L., Temmerman, A.D., Verbeke, M.: Towards contextual, cost-efficient predictive maintenance in heavy-duty trucks. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14642 LNCS, pp. 260\u2013267. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-58553-1_21","DOI":"10.1007\/978-3-031-58553-1_21"},{"key":"18_CR2","doi-asserted-by":"publisher","unstructured":"Chatterjee, J., Dethlefs, N.: Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines. Wind Energy 23(8), 1693\u20131710 (2020). https:\/\/doi.org\/10.1002\/we.2510. https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/we.2510","DOI":"10.1002\/we.2510"},{"key":"18_CR3","doi-asserted-by":"publisher","unstructured":"Fernandes, M., Corchado, J.M., Marreiros, G.: Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review. Appl. Intell. 52, 14246\u201314280 (2022). https:\/\/doi.org\/10.1007\/S10489-022-03344-3\/FIGURES\/9. https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03344-3","DOI":"10.1007\/S10489-022-03344-3\/FIGURES\/9"},{"key":"18_CR4","doi-asserted-by":"publisher","unstructured":"Gama, J., Ribeiro, R.P., Mastelini, S., Davari, N., Veloso, B.: From fault detection to anomaly explanation: a case study on predictive maintenance. J. Web Semant. 81, 100821 (2024). https:\/\/doi.org\/10.1016\/j.websem.2024.100821","DOI":"10.1016\/j.websem.2024.100821"},{"key":"18_CR5","doi-asserted-by":"publisher","unstructured":"Hafeez, A.B., Alonso, E., Riaz, A.: Dtc-trangru: improving the performance of the next-dtc prediction model with transformer and gru. In: Proceedings of the ACM Symposium on Applied Computing, pp. 927\u2013934 (2024). https:\/\/doi.org\/10.1145\/3605098.3635962. https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3605098.3635962","DOI":"10.1145\/3605098.3635962"},{"key":"18_CR6","doi-asserted-by":"publisher","unstructured":"Katreddi, S., Kasani, S., Thiruvengadam, A.: A Review of Applications of Artificial Intelligence in Heavy Duty Trucks (2022). https:\/\/doi.org\/10.3390\/en15207457. https:\/\/192.147.130.58\/chcservices\/services\/redirect?u=http:\/\/help.adobe.com&p=Reader &l=en_US &id=InternetAccess_TrustManager","DOI":"10.3390\/en15207457"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Kharazian, Z., Lindgren, T., Magn\u00fasson, S., Steinert, O., Reyna, O.A.: Scania component x dataset: a real-world multivariate time series dataset for predictive maintenance (2024). https:\/\/arxiv.org\/abs\/2401.15199","DOI":"10.1038\/s41597-025-04802-6"},{"key":"18_CR8","doi-asserted-by":"publisher","unstructured":"Lee, J., Kao, H.A., Yang, S.: Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 16, 3\u20138 (2014). https:\/\/doi.org\/10.1016\/J.PROCIR.2014.02.001","DOI":"10.1016\/J.PROCIR.2014.02.001"},{"key":"18_CR9","doi-asserted-by":"publisher","unstructured":"Math, H., Lienhart, R., Sch\u00f6n, R.: Harnessing event sensory data for error pattern prediction in vehicles: a language model approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, 19423\u201319431 (2025). https:\/\/doi.org\/10.1609\/AAAI.V39I18.34138. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/34138","DOI":"10.1609\/AAAI.V39I18.34138"},{"key":"18_CR10","doi-asserted-by":"publisher","unstructured":"Parton, M., Fois, A., Vegli\u00f2, M., Metta, C., Gregnanin, M.: Predicting the failure of component x in the scania dataset with graph neural networks. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14642 LNCS, pp. 251\u2013259. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-58553-1_20","DOI":"10.1007\/978-3-031-58553-1_20"},{"key":"18_CR11","doi-asserted-by":"publisher","unstructured":"Roelofs, C.M., Lutz, M.A., Faulstich, S., Vogt, S.: Autoencoder-based anomaly root cause analysis for wind turbines. Energy AI 4, 100065 (2021). https:\/\/doi.org\/10.1016\/j.egyai.2021.100065. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666546821000197","DOI":"10.1016\/j.egyai.2021.100065"},{"key":"18_CR12","doi-asserted-by":"publisher","unstructured":"Serradilla, O., Zugasti, E., Rodriguez, J., Zurutuza, U.: Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects. Appl. Intell. 52(10), 10934\u201310964 (2022). https:\/\/doi.org\/10.1007\/s10489-021-03004-y","DOI":"10.1007\/s10489-021-03004-y"},{"key":"18_CR13","doi-asserted-by":"publisher","unstructured":"Zhong, J., Wang, Z.: Implementing deep learning models for imminent component x failures prediction in heavy-duty scania trucks. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14642 LNCS, pp. 268\u2013276. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-58553-1_22","DOI":"10.1007\/978-3-031-58553-1_22"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05179-0_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T21:43:34Z","timestamp":1757972614000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05179-0_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"ISBN":["9783032051783","9783032051790"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05179-0_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]},"assertion":[{"value":"16 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no relevant competing interests regarding this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faro","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2025.ualg.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}