{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:50:26Z","timestamp":1742957426791,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031170294"},{"type":"electronic","value":"9783031170300"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"vor","delay-in-days":397,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Many industrial sectors are moving toward Industry Revolution (IR) 4.0. In this respect, the Internet of Things and predictive maintenance are considered the key pillars of IR 4.0. Predictive maintenance is one of the hottest trends in manufacturing where maintenance work occurs according to continuous monitoring using a healthiness check for processing equipment or instrumentation. It enables the maintenance team to have an advanced prediction of failures and allows the team to undertake timely corrective actions and decisions ahead of time. The aim of this paper is to present a smart monitoring and diagnostics system as an expert system that can alert an operator before equipment failures to prevent material and environmental damages. The main novelty and contribution of this paper is a flexible architecture of the predictive maintenance system, based on software patterns - flexible solutions to general problems. The presented conceptual model enables the integration of an expert knowledge of anticipated failures and the matrix-profile technique based anomaly detection. The results so far are encouraging.<\/jats:p>","DOI":"10.1007\/978-3-031-17030-0_3","type":"book-chapter","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T13:40:37Z","timestamp":1675258837000},"page":"26-38","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pattern Based Software Architecture for\u00a0Predictive Maintenance"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1436-1328","authenticated-orcid":false,"given":"Ants","family":"Torim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Innar","family":"Liiv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chahinez","family":"Ounoughi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8939-8948","authenticated-orcid":false,"given":"Sadok Ben","family":"Yahia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"3_CR1","unstructured":"About the Unified Modeling Language Specification Version 2.5.1. https:\/\/www.omg.org\/spec\/UML\/2.5.1"},{"key":"3_CR2","unstructured":"Arlow, J., Neustadt, I.: Enterprise Patterns and MDA: Building Better Software with Archetype Patterns and UML. Addison-Wesley Professional (Dec 2003), google-Books-ID: _fSVKDn7v04C"},{"key":"3_CR3","doi-asserted-by":"publisher","unstructured":"Cachada, A., et al.: Maintenance 4.0: intelligent and predictive maintenance system architecture. In: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), vol. 1, pp. 139\u2013146, September 2018. https:\/\/doi.org\/10.1109\/ETFA.2018.8502489, iSSN: 1946-0759","DOI":"10.1109\/ETFA.2018.8502489"},{"key":"3_CR4","unstructured":"Fowler, M.: Analysis Patterns: Reusable Object Models. Addison-Wesley Professional (1997). google-Books-ID: 4V8pZmpwmBYC"},{"key":"3_CR5","doi-asserted-by":"publisher","unstructured":"Groba, C., Cech, S., Rosenthal, F., Gossling, A.: Architecture of a predictive maintenance framework. In: 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM 2007), pp. 59\u201364, June 2007. https:\/\/doi.org\/10.1109\/CISIM.2007.14","DOI":"10.1109\/CISIM.2007.14"},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Hashemian, H.M.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Measur. 60(1), 226\u2013236, January 2011. https:\/\/doi.org\/10.1109\/TIM.2010.2047662, Conference Name: IEEE Transactions on Instrumentation and Measurement","DOI":"10.1109\/TIM.2010.2047662"},{"key":"3_CR7","unstructured":"Law, S.: Part 10: Discovering Multidimensional Time Series Motifs, May 2022. https:\/\/towardsdatascience.com\/part-10-discovering-multidimensional-time-series-motifs-45da53b594bb"},{"issue":"39","key":"3_CR8","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.21105\/joss.01504","volume":"4","author":"SM Law","year":"2019","unstructured":"Law, S.M.: Stumpy: a powerful and scalable python library for time series data mining. J. Open Source Softw. 4(39), 1504 (2019)","journal-title":"J. Open Source Softw."},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Motaghare, O., Pillai, A.S., Ramachandran, K.: Predictive maintenance architecture. In: 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1\u20134, December 2018. https:\/\/doi.org\/10.1109\/ICCIC.2018.8782406, iSSN: 2473-943X","DOI":"10.1109\/ICCIC.2018.8782406"},{"key":"3_CR10","doi-asserted-by":"publisher","unstructured":"Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings, pp. 370\u2013377, December 2002. https:\/\/doi.org\/10.1109\/ICDM.2002.1183925","DOI":"10.1109\/ICDM.2002.1183925"},{"key":"3_CR11","unstructured":"Piho, G.: Archetypes based techniques for development of domains. Requirements and Software. Towards LIMS Software Factory, Tallinn (2011)"},{"key":"3_CR12","doi-asserted-by":"publisher","unstructured":"Piho, G., Tepandi, J., Roost, M.: Domain analysis with archetype patterns based Zachman Framework for enterprise architecture. In: 2010 International Symposium on Information Technology, vol. 3, pp. 1351\u20131356, June 2010. https:\/\/doi.org\/10.1109\/ITSIM.2010.5561641, iSSN: 2155-899X","DOI":"10.1109\/ITSIM.2010.5561641"},{"key":"3_CR13","doi-asserted-by":"publisher","unstructured":"Piho, G., Tepandi, J., Thompson, D., Woerner, A., Parman, M.: Business archetypes and archetype patterns from the HL7 RIM and openEHR RM perspectives: towards interoperability and evolution of healthcare models and software systems. Procedia Comput. Sci. 63, 553\u2013560 (2015). https:\/\/doi.org\/10.1016\/j.procs.2015.08.384, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050915025193","DOI":"10.1016\/j.procs.2015.08.384"},{"key":"3_CR14","unstructured":"Ran, Y., Zhou, X., Lin, P., Wen, Y., Deng, R.: A survey of predictive maintenance: systems, purposes and approaches. arXiv:1912.07383 [cs, eess] (December 2019), arXiv: 1912.07383"},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"Yeh, C.C.M., Kavantzas, N., Keogh, E.: Matrix Profile VI: Meaningful Multidimensional Motif Discovery, pp. 565\u2013574, November 2017. https:\/\/doi.org\/10.1109\/ICDM.2017.66","DOI":"10.1109\/ICDM.2017.66"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Yeh, C.C.M., et al.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1317\u20131322 (2016). https:\/\/doi.org\/10.1109\/ICDM.2016.0179","DOI":"10.1109\/ICDM.2016.0179"},{"key":"3_CR17","unstructured":"Zachman, J.A.: The Zachman Framework for Enterprise Architecture. Primer for Enterprise Engineering and Manufacturing.[si]: Zachman International (2003)"}],"container-title":["Communications in Computer and Information Science","Nordic Artificial Intelligence Research and Development"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17030-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T14:12:46Z","timestamp":1675260766000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17030-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031170294","9783031170300"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17030-0_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Symposium of the Norwegian AI Society","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Oslo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nais12022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aisociety.no\/nais2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}