{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T07:20:35Z","timestamp":1772781635303,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031539688","type":"print"},{"value":"9783031539695","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-53969-5_22","type":"book-chapter","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T09:03:35Z","timestamp":1707987815000},"page":"288-302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Data-Driven Monitoring Approach for\u00a0Diagnosing Quality Degradation in\u00a0a\u00a0Glass Container Process"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5751-6966","authenticated-orcid":false,"given":"Maria Alexandra","family":"Oliveira","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6293-5691","authenticated-orcid":false,"given":"Lu\u00eds","family":"Guimar\u00e3es","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9946-5614","authenticated-orcid":false,"given":"Jos\u00e9 Lu\u00eds","family":"Borges","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0815-1068","authenticated-orcid":false,"given":"Bernardo","family":"Almada-Lobo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"22_CR1","unstructured":"Bothe, D.R.: Measuring Process Capability: Techniques and Calculations for Quality and Manufacturing Engineers. McGraw-Hill (1997)"},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.jprocont.2015.02.004","volume":"28","author":"LH Chiang","year":"2015","unstructured":"Chiang, L.H., Jiang, B., Zhu, X., Huang, D., Braatz, R.D.: Diagnosis of multiple and unknown faults using the causal map and multivariate statistics. J. Process Control 28, 27\u201339 (2015)","journal-title":"J. Process Control"},{"key":"22_CR3","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.cie.2017.11.029","volume":"115","author":"ZL Chong","year":"2018","unstructured":"Chong, Z.L., Mukherjee, A., Khoo, M.B.: Some distribution-free Lepage-type schemes for simultaneous monitoring of one-sided shifts in location and scale. Comput. Ind. Eng. 115, 653\u2013669 (2018)","journal-title":"Comput. Ind. Eng."},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.cie.2018.12.066","volume":"128","author":"N Chukhrova","year":"2019","unstructured":"Chukhrova, N., Johannssen, A.: Improved control charts for fraction non-conforming based on hypergeometric distribution. Comput. Ind. Eng. 128, 795\u2013806 (2019)","journal-title":"Comput. Ind. Eng."},{"key":"22_CR5","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.compchemeng.2017.05.029","volume":"106","author":"D Ha","year":"2017","unstructured":"Ha, D., Ahmed, U., Pyun, H., Lee, C.J., Baek, K.H., Han, C.: Multi-mode operation of principal component analysis with k-nearest neighbor algorithm to monitor compressors for liquefied natural gas mixed refrigerant processes. Comput. Chem. Eng. 106, 96\u2013105 (2017)","journal-title":"Comput. Chem. Eng."},{"issue":"18","key":"22_CR6","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.ifacol.2018.09.380","volume":"51","author":"S Heo","year":"2018","unstructured":"Heo, S., Lee, J.H.: Fault detection and classification using artificial neural networks. IFAC-PapersOnLine 51(18), 470\u2013475 (2018)","journal-title":"IFAC-PapersOnLine"},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1108\/RPJ-05-2019-0121","volume":"26","author":"H Hu","year":"2019","unstructured":"Hu, H., He, K., Zhong, T., Hong, Y.: Fault diagnosis of FDM process based on support vector machine (SVM). Rapid Prototyping J. 26, 330\u2013348 (2019)","journal-title":"Rapid Prototyping J."},{"issue":"2","key":"22_CR8","doi-asserted-by":"publisher","first-page":"335","DOI":"10.3390\/pr10020335","volume":"10","author":"C Ji","year":"2022","unstructured":"Ji, C., Sun, W.: A review on data-driven process monitoring methods: characterization and mining of industrial data. Processes 10(2), 335 (2022)","journal-title":"Processes"},{"key":"22_CR9","doi-asserted-by":"publisher","first-page":"106756","DOI":"10.1016\/j.compchemeng.2020.106756","volume":"136","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Bhattacharya, A., Flores-Cerrillo, J.: Data-driven process monitoring and fault analysis of reformer units in hydrogen plants: industrial application and perspectives. Comput. Chem. Eng. 136, 106756 (2020)","journal-title":"Comput. Chem. Eng."},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"107064","DOI":"10.1016\/j.compchemeng.2020.107064","volume":"142","author":"H Lee","year":"2020","unstructured":"Lee, H., Kim, C., Lim, S., Lee, J.M.: Data-driven fault diagnosis for chemical processes using transfer entropy and graphical lasso. Comput. Chem. Eng. 142, 107064 (2020)","journal-title":"Comput. Chem. Eng."},{"key":"22_CR11","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.cie.2019.01.013","volume":"129","author":"C Li","year":"2019","unstructured":"Li, C., Mukherjee, A., Su, Q.: A distribution-free phase i monitoring scheme for subgroup location and scale based on the multi-sample Lepage statistic. Comput. Ind. Eng. 129, 259\u2013273 (2019)","journal-title":"Comput. Ind. Eng."},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.jprocont.2020.09.006","volume":"95","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Chen, H.S., Wu, H., Dai, Y., Yao, Y., Yan, Z.: Simplified granger causality map for data-driven root cause diagnosis of process disturbances. J. Process Control 95, 45\u201354 (2020)","journal-title":"J. Process Control"},{"key":"22_CR13","volume-title":"Introduction to Statistical Quality Control","author":"DC Montgomery","year":"2020","unstructured":"Montgomery, D.C.: Introduction to Statistical Quality Control. Wiley, Hoboken (2020)"},{"issue":"4","key":"22_CR14","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1515\/revce-2017-0069","volume":"36","author":"NM Nor","year":"2020","unstructured":"Nor, N.M., Hassan, C.R.C., Hussain, M.A.: A review of data-driven fault detection and diagnosis methods: applications in chemical process systems. Rev. Chem. Eng. 36(4), 513\u2013553 (2020)","journal-title":"Rev. Chem. Eng."},{"key":"22_CR15","doi-asserted-by":"publisher","first-page":"106131","DOI":"10.1016\/j.cie.2019.106131","volume":"139","author":"RDC Quinino","year":"2020","unstructured":"Quinino, R.D.C., Cruz, F.R., Ho, L.L.: Attribute inspection control charts for the joint monitoring of mean and variance. Comput. Ind. Eng. 139, 106131 (2020)","journal-title":"Comput. Ind. Eng."},{"issue":"3","key":"22_CR16","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3390\/pr5030035","volume":"5","author":"MS Reis","year":"2017","unstructured":"Reis, M.S., Gins, G.: Industrial process monitoring in the big data\/industry 4.0 era: from detection, to diagnosis, to prognosis. Processes 5(3), 35 (2017)","journal-title":"Processes"},{"issue":"4","key":"22_CR17","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1080\/00224065.2019.1569954","volume":"51","author":"MS Reis","year":"2019","unstructured":"Reis, M.S., Gins, G., Rato, T.J.: Incorporation of process-specific structure in statistical process monitoring: a review. J. Qual. Technol. 51(4), 407\u2013421 (2019)","journal-title":"J. Qual. Technol."},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Sun, J., Zhou, S., Veeramani, D.: A neural network-based control chart for monitoring and interpreting autocorrelated multivariate processes using layer-wise relevance propagation. Qual. Eng. 1\u201315 (2022)","DOI":"10.1080\/08982112.2022.2087041"},{"key":"22_CR19","doi-asserted-by":"publisher","first-page":"106991","DOI":"10.1016\/j.compchemeng.2020.106991","volume":"141","author":"W Sun","year":"2020","unstructured":"Sun, W., Paiva, A.R., Xu, P., Sundaram, A., Braatz, R.D.: Fault detection and identification using Bayesian recurrent neural networks. Comput. Chem. Eng. 141, 106991 (2020)","journal-title":"Comput. Chem. Eng."},{"key":"22_CR20","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.neucom.2016.09.076","volume":"228","author":"K Yan","year":"2017","unstructured":"Yan, K., Ji, Z., Shen, W.: Online fault detection methods for chillers combining extended Kalman filter and recursive one-class SVM. Neurocomputing 228, 205\u2013212 (2017)","journal-title":"Neurocomputing"},{"key":"22_CR21","doi-asserted-by":"publisher","first-page":"105304","DOI":"10.1016\/j.conengprac.2022.105304","volume":"127","author":"WT Yang","year":"2022","unstructured":"Yang, W.T., Reis, M.S., Borodin, V., Juge, M., Roussy, A.: An interpretable unsupervised Bayesian network model for fault detection and diagnosis. Control. Eng. Pract. 127, 105304 (2022)","journal-title":"Control. Eng. Pract."},{"key":"22_CR22","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.cie.2017.06.027","volume":"110","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Li, E., Li, Z.: A cram\u00e9r-von mises test-based distribution-free control chart for joint monitoring of location and scale. Comput. Ind. Eng. 110, 484\u2013497 (2017)","journal-title":"Comput. Ind. Eng."},{"key":"22_CR23","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.jprocont.2018.02.004","volume":"64","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Jiang, T., Li, S., Yang, Y.: Automated feature learning for nonlinear process monitoring-an approach using stacked denoising autoencoder and k-nearest neighbor rule. J. Process Control 64, 49\u201361 (2018)","journal-title":"J. Process Control"},{"key":"22_CR24","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.compchemeng.2017.02.041","volume":"107","author":"Z Zhang","year":"2017","unstructured":"Zhang, Z., Zhao, J.: A deep belief network based fault diagnosis model for complex chemical processes. Comput. Chem. Eng. 107, 395\u2013407 (2017)","journal-title":"Comput. Chem. Eng."},{"issue":"7","key":"22_CR25","doi-asserted-by":"publisher","first-page":"2574","DOI":"10.1021\/acs.iecr.7b03771","volume":"57","author":"W Zhu","year":"2018","unstructured":"Zhu, W., Sun, W., Romagnoli, J.: Adaptive k-nearest-neighbor method for process monitoring. Ind. Eng. Chem. Res. 57(7), 2574\u20132586 (2018)","journal-title":"Ind. Eng. Chem. Res."}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53969-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T09:08:10Z","timestamp":1707988090000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53969-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031539688","9783031539695"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53969-5_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"16 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grasmere","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2023.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"In-house system and EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"119","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":"72","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":"61% - 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":"5-6","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":"1-2","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)"}}]}}