{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:22:09Z","timestamp":1743006129471,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030843366"},{"type":"electronic","value":"9783030843373"}],"license":[{"start":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T00:00:00Z","timestamp":1628380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T00:00:00Z","timestamp":1628380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-84337-3_2","type":"book-chapter","created":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T17:04:06Z","timestamp":1628355846000},"page":"15-26","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Three-Step Machine Learning Pipeline for Detecting and Explaining Anomalies in the Time Series of Industrial Process Plants"],"prefix":"10.1007","author":[{"given":"Marcel","family":"Dix","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,8]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Kl\u00f6pper, B., et al.: Defining software architectures for big data enabled operator support systems. In: IEEE 14th International Conference on Industrial Informatics (INDIN) (2016)","key":"2_CR1","DOI":"10.1109\/INDIN.2016.7819366"},{"issue":"09","key":"2_CR2","doi-asserted-by":"publisher","first-page":"62","DOI":"10.17560\/atp.v58i09.580","volume":"58","author":"M Atzm\u00fcller","year":"2016","unstructured":"Atzm\u00fcller, M., et al.: Big data analytics for proactive industrial decision support \u2013 approaches and first experiences in the FEE project. atp mag. 58(09), 62\u201374 (2016)","journal-title":"atp mag."},{"doi-asserted-by":"crossref","unstructured":"Sayda, A.F., Taylor, J.H.: Modeling and control of three-phase gravity separators in oil production facilities. In: 2007 American Control Conference, New York, USA (2007)","key":"2_CR3","DOI":"10.1109\/ACC.2007.4282265"},{"unstructured":"Dix, M., Kl\u00f6pper, B., Blanchon, J.-C., Thorud, E.: A formula for accelerating autonomous anomaly detection. ABB Review 02\/2021, pp. 14\u201317 (2021)","key":"2_CR4"},{"issue":"9","key":"2_CR5","doi-asserted-by":"publisher","first-page":"1843","DOI":"10.3182\/20130619-3-RU-3018.00057","volume":"46","author":"L Abele","year":"2013","unstructured":"Abele, L., Anic, M., Gutmann, T., Folmer, J., Kleinsteuber, M., Vogel-Heuser, B.: Combining knowledge modeling and machine learning for alarm root cause analysis. IFAC Proc. Vol. 46(9), 1843\u20131848 (2013)","journal-title":"IFAC Proc. Vol."},{"key":"2_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/978-3-030-32236-6_51","volume-title":"natural language processing and Chinese computing","author":"F Xu","year":"2019","unstructured":"Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., Zhu, J.: Explainable AI: a brief survey on history, research areas, approaches and challenges. In: Tang, J., Kan, M.-Y., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2019. LNCS (LNAI), vol. 11839, pp. 563\u2013574. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32236-6_51"},{"unstructured":"Kotriwala, A., Kloepper, B., Dix, M., Gopalakrishnan, G., Ziobro, D., Potschka, A.: XAI for operations in the process industry \u2013 applications, theses, and research directions. In: Proceedings of the Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice, AAAI\u00adMAKE (2021)","key":"2_CR7"},{"issue":"7","key":"2_CR8","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.1109\/JIOT.2019.2958185","volume":"7","author":"AA Cook","year":"2019","unstructured":"Cook, A.A., M\u0131s\u0131rl\u0131, G., Fan, Z.: Anomaly detection for IoT time-series data: a survey. IEEE Internet Things J. 7(7), 6481\u20136494 (2019)","journal-title":"IEEE Internet Things J."},{"doi-asserted-by":"crossref","unstructured":"Sakurada, M.; Yairi, T. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysia (2014)","key":"2_CR9","DOI":"10.1145\/2689746.2689747"},{"doi-asserted-by":"crossref","unstructured":"Ashraf, J., Bakhshi, A.D., Moustafa, N., Khurshid, H., Javed, A., Beheshti, A.: Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. (2020)","key":"2_CR10","DOI":"10.1109\/TITS.2020.3017882"},{"doi-asserted-by":"crossref","unstructured":"Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., Pei, D.: Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2828\u20132837 (2019)","key":"2_CR11","DOI":"10.1145\/3292500.3330672"},{"doi-asserted-by":"crossref","unstructured":"Zhong, S., Fu, S., Lin, L., Fu, X., Cui, Z., Wang, R.: A novel unsupervised anomaly detection for gas turbine using isolation forest. In: 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 1\u20136 (2019)","key":"2_CR12","DOI":"10.1109\/ICPHM.2019.8819409"},{"unstructured":"Homepage of public-funded research project FEE. https:\/\/www.fee-projekt.de\/index_en.html. Accessed 10 Feb 2021","key":"2_CR13"},{"key":"2_CR14","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-658-19287-7_13","volume-title":"Data Science-Analytics and Applications","author":"M Atzm\u00fcller","year":"2017","unstructured":"Atzm\u00fcller, M., Arnu, D., Schmidt, A.: Anomaly detection and structural analysis in industrial production environments. In: Haber, P., Lampoltshammer, T., Mayr, M. (eds.) Data Science-Analytics and Applications, pp. 91\u201395. Springer, Wiesbaden (2017). https:\/\/doi.org\/10.1007\/978-3-658-19287-7_13"},{"issue":"1","key":"2_CR15","first-page":"14","volume":"6","author":"SP Siddharthan","year":"2020","unstructured":"Siddharthan, S.P., Dix, M., Sprick, B., Kl\u00f6pper, B.: Summarizing industrial log data with latent Dirichlet allocation. Arch. Data Sci. Ser. A 6(1), 14 (2020)","journal-title":"Arch. Data Sci. Ser. A"},{"unstructured":"Scikit-learn Novelty and Outlier Detection Homepage. https:\/\/scikit-learn.org\/stable\/modules\/outlier_detection.html. Accessed 14 Feb 2021","key":"2_CR16"},{"unstructured":"Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874 (2017)","key":"2_CR17"},{"doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you?: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","key":"2_CR18","DOI":"10.1145\/2939672.2939778"},{"issue":"3","key":"2_CR19","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3236386.3241340","volume":"16","author":"ZC Lipton","year":"2018","unstructured":"Lipton, Z.C.: The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31\u201357 (2018)","journal-title":"Queue"},{"issue":"1","key":"2_CR20","doi-asserted-by":"publisher","first-page":"162","DOI":"10.21629\/JSEE.2017.01.18","volume":"28","author":"B Zhao","year":"2017","unstructured":"Zhao, B., Lu, H., Chen, S., Liu, J., Wu, D.: Convolutional neural networks for time series classification. J. Syst. Eng. Electron. 28(1), 162\u2013169 (2017)","journal-title":"J. Syst. Eng. Electron."},{"unstructured":"CORYS Indiss Plus Homepage. https:\/\/www.corys.com\/en\/indiss-plusr. Accessed 15 May 2021","key":"2_CR21"},{"unstructured":"System 800xA Simulator Homepage. https:\/\/new.abb.com\/control-systems\/service\/customer-support\/800xA-services\/800xA-training\/800xa-simulator. Accessed 10 Feb 2021","key":"2_CR22"}],"container-title":["Lecture Notes in Networks and Systems","The International Conference on Deep Learning, Big Data and Blockchain (Deep-BDB 2021)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-84337-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T17:04:28Z","timestamp":1628355868000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-84337-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,8]]},"ISBN":["9783030843366","9783030843373"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-84337-3_2","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2021,8,8]]},"assertion":[{"value":"8 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Deep-BDB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The International Conference on Deep Learning, Big Data and Blockchain","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"deepbdb2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}