{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:49:59Z","timestamp":1761130199672,"version":"3.27.0"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,10]]},"DOI":"10.1109\/etfa61755.2024.10710647","type":"proceedings-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T17:51:22Z","timestamp":1729101082000},"page":"1-8","source":"Crossref","is-referenced-by-count":4,"title":["Extracting Knowledge using Machine Learning for Anomaly Detection and Root-Cause Diagnosis"],"prefix":"10.1109","volume":"21","author":[{"given":"Lukas","family":"Moddemann","sequence":"first","affiliation":[{"name":"Institute of Automation Technology, Helmut-Schmidt-University,Hamburg,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henrik Sebastian","family":"Steude","sequence":"additional","affiliation":[{"name":"Institute of Automation Technology, Helmut-Schmidt-University,Hamburg,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Diedrich","sequence":"additional","affiliation":[{"name":"Institute of Automation Technology, Helmut-Schmidt-University,Hamburg,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ingo","family":"Pill","sequence":"additional","affiliation":[{"name":"Institute of Software Technology, Graz University of Technology,Graz,Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Niggemann","sequence":"additional","affiliation":[{"name":"Institute of Automation Technology, Helmut-Schmidt-University,Hamburg,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.protcy.2014.09.083"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(87)90063-4"},{"issue":"2","key":"ref3","first-page":"57","article-title":"Model-Based Diagnosis under Real-World Constraints","volume":"21","author":"Darwiche","year":"2000","journal-title":"AI magazine"},{"key":"ref4","article-title":"Discret2Di-Deep Learning based Discretization for Model-based Di-agnosis","author":"Moddemann","year":"2023","journal-title":"arXiv preprint"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-46430-1_25"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/c2016-0-00862-8"},{"key":"ref7","article-title":"Variational Recurrent Auto-Encoders","author":"Fabius","year":"2014","journal-title":"arXiv preprint"},{"volume-title":"Studies in Hybrid Systems: Modeling, Analysis, and Control","year":"1995","author":"Branicky","key":"ref8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(87)90062-2"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ISSREW.2015.7392050"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1613\/jair.4503"},{"key":"ref12","first-page":"1039","article-title":"The Route to Success: A Performance Comparison of Diagnosis Algorithms","volume-title":"23rd International Joint Conference on Artificial Intelligence, ser. IJCAI \u201813","author":"Nica"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(84)90038-9"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/34.865189"},{"key":"ref15","article-title":"Deep Unsuper-vised Clustering U sing Mixture of Autoencoders","author":"Zhang","year":"2017","journal-title":"arXiv preprint"},{"key":"ref16","article-title":"beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework","author":"Higgins","year":"2017","journal-title":"ICLR"},{"key":"ref17","article-title":"Categorical Reparameterization with Gumbel-Softmax","author":"Jang","year":"2016","journal-title":"arXiv preprint"},{"key":"ref18","article-title":"A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning","volume":"30","author":"Fraccaro","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30490-4_56"},{"issue":"86","key":"ref20","first-page":"2579","article-title":"Visualizing Data using t -SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3105827"},{"key":"ref22","article-title":"Diagnosis driven Anomaly Detection for CPS","author":"Steude","year":"2023","journal-title":"arXiv preprint"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103498"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053558"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-016-5565-9"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-58473-0_144"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/QEST.2004.1348029"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s11334-022-00449-3"},{"key":"ref30","first-page":"487","article-title":"Fast Algorithms for Mining Association Rules","volume-title":"Proc. 20th int. conf. very large data bases, VLDB","volume":"1215","author":"Agrawal"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335372"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/323"},{"key":"ref33","first-page":"1053","article-title":"Behavioral Diagnosis of LTL Specifications at Operator Level","volume-title":"23rd International Joint Conference on Artificial Intelligence, ser. IJCAI \u201813. AAAI Press","author":"Pill"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-016-0987-z"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ETFA52439.2022.9921546"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2022.07.099"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1017\/cbo9780511809071"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3105827"}],"event":{"name":"2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)","start":{"date-parts":[[2024,9,10]]},"location":"Padova, Italy","end":{"date-parts":[[2024,9,13]]}},"container-title":["2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10710336\/10710347\/10710647.pdf?arnumber=10710647","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T05:15:09Z","timestamp":1729142109000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10710647\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,10]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/etfa61755.2024.10710647","relation":{},"subject":[],"published":{"date-parts":[[2024,9,10]]}}}