{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:02:51Z","timestamp":1771524171244,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2019R1F1A1064125"],"award-info":[{"award-number":["2019R1F1A1064125"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s10845-022-02057-1","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T07:47:40Z","timestamp":1669967260000},"page":"521-537","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Rule-based visualization of faulty process conditions in the die-casting manufacturing"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4021-7672","authenticated-orcid":false,"given":"Josue","family":"Obregon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4850-6284","authenticated-orcid":false,"given":"Jae-Yoon","family":"Jung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,2]]},"reference":[{"key":"2057_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138\u201352160. https:\/\/doi.org\/10.1109\/ACCESS.2018.2870052","journal-title":"IEEE Access"},{"key":"2057_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/J.INFFUS.2019.12.012","volume":"58","author":"A Barredo Arrieta","year":"2020","unstructured":"Barredo Arrieta, A., D\u00edaz-Rodr\u00edguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82\u2013115. https:\/\/doi.org\/10.1016\/J.INFFUS.2019.12.012","journal-title":"Information Fusion"},{"issue":"4","key":"2057_CR3","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1080\/03081070902857563","volume":"38","author":"R Belohlavek","year":"2009","unstructured":"Belohlavek, R., de Baets, B., Outrata, J., & Vychodil, V. (2009). Inducing decision trees via concept lattices. International Journal of General Systems, 38(4), 455\u2013467. https:\/\/doi.org\/10.1080\/03081070902857563","journal-title":"International Journal of General Systems"},{"issue":"5","key":"2057_CR4","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1007\/S11837-015-1333-8","volume":"67","author":"F Bonollo","year":"2015","unstructured":"Bonollo, F., Gramegna, N., & Timelli, G. (2015). High-pressure die-casting: Contradictions and challenges. JOM Journal of the Minerals Metals and Materials Society, 67(5), 901\u2013908. https:\/\/doi.org\/10.1007\/S11837-015-1333-8","journal-title":"JOM Journal of the Minerals Metals and Materials Society"},{"issue":"421","key":"2057_CR5","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24(421), 123\u2013140. https:\/\/doi.org\/10.1007\/BF00058655","journal-title":"Machine Learning"},{"issue":"1","key":"2057_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Machine Learning"},{"key":"2057_CR7","doi-asserted-by":"publisher","unstructured":"Carletti, M., Masiero, C., Beghi, A., & Susto, G. A. (2019). Explainable machine learning in industry 4.0: Evaluating feature importance in anomaly detection to enable root cause analysis. Conference Proceedings\u2014IEEE International Conference on Systems, Man and Cybernetics, 2019-October, 21\u201326. https:\/\/doi.org\/10.1109\/SMC.2019.8913901","DOI":"10.1109\/SMC.2019.8913901"},{"issue":"4","key":"2057_CR8","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/S0020-7373(87)80003-2","volume":"27","author":"J Cendrowska","year":"1987","unstructured":"Cendrowska, J. (1987). PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies, 27(4), 349\u2013370. https:\/\/doi.org\/10.1016\/S0020-7373(87)80003-2","journal-title":"International Journal of Man-Machine Studies"},{"key":"2057_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/JAIR.953","volume":"16","author":"Nv Chawla","year":"2002","unstructured":"Chawla, Nv., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321\u2013357. https:\/\/doi.org\/10.1613\/JAIR.953","journal-title":"Journal of Artificial Intelligence Research"},{"key":"2057_CR10","unstructured":"Chen, C.-L. (1997). Evaluation of aluminum die casting defects causing casting rejection during machining. Doctoral dissertation, Ohio State University. Retrieved August 2, 2022, from http:\/\/rave.ohiolink.edu\/etdc\/view?acc_num=osu1155309911"},{"key":"2057_CR11","doi-asserted-by":"publisher","unstructured":"Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. 13\u201317-Augu, pp. 785\u2013794). New York, NY, USA: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"2057_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","volume":"1857","author":"TG Dietterich","year":"2000","unstructured":"Dietterich, T. G. (2000). Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems (MCS 2000). Lecture Notes in Computer Science, 1857, 1\u201315. https:\/\/doi.org\/10.1007\/3-540-45014-9_1","journal-title":"International Workshop on Multiple Classifier Systems (MCS 2000). Lecture Notes in Computer Science"},{"issue":"5","key":"2057_CR13","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.2307\/2699986","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189\u20131232. https:\/\/doi.org\/10.2307\/2699986","journal-title":"Annals of Statistics"},{"key":"2057_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-59830-2","volume-title":"Formal concept analysis","author":"B Ganter","year":"1999","unstructured":"Ganter, B., & Wille, R. (1999). Formal concept analysis. Springer. https:\/\/doi.org\/10.1007\/978-3-642-59830-2"},{"issue":"2","key":"2057_CR15","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/S10845-021-01890-0\/FIGURES\/7","volume":"33","author":"A Gerling","year":"2022","unstructured":"Gerling, A., Ziekow, H., Hess, A., Schreier, U., Seiffer, C., & Abdeslam, D. O. (2022). Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric. Journal of Intelligent Manufacturing, 33(2), 555\u2013573. https:\/\/doi.org\/10.1007\/S10845-021-01890-0\/FIGURES\/7","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"1","key":"2057_CR16","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1080\/10618600.2014.907095","volume":"24","author":"A Goldstein","year":"2015","unstructured":"Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015). Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal of Computational and Graphical Statistics, 24(1), 44\u201365. https:\/\/doi.org\/10.1080\/10618600.2014.907095","journal-title":"Journal of Computational and Graphical Statistics"},{"issue":"5","key":"2057_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1\u201342. https:\/\/doi.org\/10.1145\/3236009","journal-title":"ACM Computing Surveys"},{"key":"2057_CR18","volume-title":"The elements of statistical learning data mining, inference, and prediction (Second)","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning data mining, inference, and prediction (Second). Springer."},{"key":"2057_CR19","doi-asserted-by":"publisher","first-page":"2471","DOI":"10.1007\/S10845-021-01792-1","volume":"33","author":"M Ismail","year":"2021","unstructured":"Ismail, M., Mostafa, N. A., & El-assal, A. (2021). Quality monitoring in multistage manufacturing systems by using machine learning techniques. Journal of Intelligent Manufacturing, 33, 2471\u20132486. https:\/\/doi.org\/10.1007\/S10845-021-01792-1","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2057_CR45","doi-asserted-by":"publisher","unstructured":"Kang, B., Wook Cho, N., Kang, S. H., & Jung, J. Y. (2011). Real-time business process monitoring using formal concept analysis.  ndustrial Management & Data Systems, 111(5), 652\u2013674. https:\/\/doi.org\/10.1108\/02635571111137241","DOI":"10.1108\/02635571111137241"},{"key":"2057_CR20","first-page":"3146","volume-title":"Advances in Neural Information Processing Systems","author":"G Ke","year":"2017","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In I. Guyon, U. v Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 30, pp. 3146\u20133154). Curran Associates, Inc."},{"issue":"8","key":"2057_CR21","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1080\/0951192X.2017.1407447","volume":"31","author":"A Kim","year":"2018","unstructured":"Kim, A., Oh, K., Jung, J.-Y., & Kim, B. (2018). Imbalanced classification of manufacturing quality conditions using cost-sensitive decision tree ensembles. International Journal of Computer Integrated Manufacturing, 31(8), 701\u2013717. https:\/\/doi.org\/10.1080\/0951192X.2017.1407447","journal-title":"International Journal of Computer Integrated Manufacturing"},{"issue":"1","key":"2057_CR22","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/S40962-021-00606-7","volume":"16","author":"AE Kopper","year":"2021","unstructured":"Kopper, A. E., & Apelian, D. (2021). Predicting quality of castings via supervised learning method. International Journal of Metalcasting, 16(1), 93\u2013105. https:\/\/doi.org\/10.1007\/S40962-021-00606-7","journal-title":"International Journal of Metalcasting"},{"key":"2057_CR23","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/J.JMSY.2017.03.001","volume":"43","author":"D Kozjek","year":"2017","unstructured":"Kozjek, D., Vrabi\u010d, R., Kralj, D., & Butala, P. (2017). Interpretative identification of the faulty conditions in a cyclic manufacturing process. Journal of Manufacturing Systems, 43, 214\u2013224. https:\/\/doi.org\/10.1016\/J.JMSY.2017.03.001","journal-title":"Journal of Manufacturing Systems"},{"key":"2057_CR24","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/978-3-540-24651-0_25","volume":"2961","author":"SO Kuznetsov","year":"2004","unstructured":"Kuznetsov, S. O. (2004). Machine learning and formal concept analysis. Lecture Notes in Artificial Intelligence (subseries of Lecture Notes in Computer Science), 2961, 287\u2013312. https:\/\/doi.org\/10.1007\/978-3-540-24651-0_25","journal-title":"Lecture Notes in Artificial Intelligence (subseries of Lecture Notes in Computer Science)"},{"issue":"3","key":"2057_CR25","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1002\/WIDM.1088","volume":"3","author":"SO Kuznetsov","year":"2013","unstructured":"Kuznetsov, S. O., & Poelmans, J. (2013). Knowledge representation and processing with formal concept analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(3), 200\u2013215. https:\/\/doi.org\/10.1002\/WIDM.1088","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"issue":"5","key":"2057_CR26","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1007\/S10845-020-01711-W","volume":"33","author":"CY Lee","year":"2020","unstructured":"Lee, C. Y., & Chien, C. F. (2020). Pitfalls and protocols of data science in manufacturing practice. Journal of Intelligent Manufacturing, 33(5), 1189\u20131207. https:\/\/doi.org\/10.1007\/S10845-020-01711-W","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2057_CR27","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/J.JMSY.2020.10.013","volume":"57","author":"J Lee","year":"2020","unstructured":"Lee, J., Lee, Y. C., & Kim, J. T. (2020). Fault detection based on one-class deep learning for manufacturing applications limited to an imbalanced database. Journal of Manufacturing Systems, 57, 357\u2013366. https:\/\/doi.org\/10.1016\/J.JMSY.2020.10.013","journal-title":"Journal of Manufacturing Systems"},{"key":"2057_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/J.JMATPROTEC.2020.116972","volume":"290","author":"J Lee","year":"2021","unstructured":"Lee, J., Lee, Y. C., & Kim, J. T. (2021a). Migration from the traditional to the smart factory in the die-casting industry: Novel process data acquisition and fault detection based on artificial neural network. Journal of Materials Processing Technology, 290, 116972. https:\/\/doi.org\/10.1016\/J.JMATPROTEC.2020.116972","journal-title":"Journal of Materials Processing Technology"},{"issue":"1747\u20131759","key":"2057_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S10845-021-01758-3","volume":"33","author":"M Lee","year":"2021","unstructured":"Lee, M., Jeon, J., & Lee, H. (2021b). Explainable AI for domain experts: A post Hoc analysis of deep learning for defect classification of TFT\u2013LCD panels. Journal of Intelligent Manufacturing, 33(1747\u20131759), 1\u201313. https:\/\/doi.org\/10.1007\/S10845-021-01758-3","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"6","key":"2057_CR30","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/S13042-013-0150-Z","volume":"4","author":"J Li","year":"2013","unstructured":"Li, J., Mei, C., Kumar, C. A., & Zhang, X. (2013). On rule acquisition in decision formal contexts. International Journal of Machine Learning and Cybernetics, 4(6), 721\u2013731. https:\/\/doi.org\/10.1007\/S13042-013-0150-Z","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"2057_CR48","doi-asserted-by":"publisher","unstructured":"Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 5667. https:\/\/doi.org\/10.1038\/s42256-019-0138-9","DOI":"10.1038\/s42256-019-0138-9"},{"key":"2057_CR31","unstructured":"Lundberg, S. M. & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-Decem (Section 2), pp 4766\u20134775"},{"issue":"10","key":"2057_CR32","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1016\/J.JPROCONT.2009.07.011","volume":"19","author":"S Mahadevan","year":"2009","unstructured":"Mahadevan, S., & Shah, S. L. (2009). Fault detection and diagnosis in process data using one-class support vector machines. Journal of Process Control, 19(10), 1627\u20131639. https:\/\/doi.org\/10.1016\/J.JPROCONT.2009.07.011","journal-title":"Journal of Process Control"},{"key":"2057_CR33","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/J.JMSY.2021.07.001","volume":"60","author":"J Obregon","year":"2021","unstructured":"Obregon, J., Hong, J., & Jung, J.-Y. (2021). Rule-based explanations based on ensemble machine learning for detecting sink mark defects in the injection moulding process. Journal of Manufacturing Systems, 60, 392\u2013405. https:\/\/doi.org\/10.1016\/J.JMSY.2021.07.001","journal-title":"Journal of Manufacturing Systems"},{"key":"2057_CR34","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/J.INFFUS.2022.08.021","volume":"89","author":"J Obregon","year":"2023","unstructured":"Obregon, J., & Jung, J.-Y. (2023). RuleCOSI+: Rule extraction for interpreting classification tree ensembles. Information Fusion, 89, 355\u2013381. https:\/\/doi.org\/10.1016\/J.INFFUS.2022.08.021","journal-title":"Information Fusion"},{"key":"2057_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.02.012","author":"J Obregon","year":"2019","unstructured":"Obregon, J., Kim, A., & Jung, J.-Y. (2019). RuleCOSI: Combination and simplification of production rules from boosted decision trees for imbalanced classification. Expert Systems with Applications. https:\/\/doi.org\/10.1016\/j.eswa.2019.02.012","journal-title":"Expert Systems with Applications"},{"key":"2057_CR46","doi-asserted-by":"publisher","unstructured":"Oh, S. (2019). Feature interaction in terms of prediction performance. Applied Sciences, 9(23), 5191. https:\/\/doi.org\/10.3390\/APP9235191","DOI":"10.3390\/APP9235191"},{"key":"2057_CR36","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1145\/2939672.2939778","volume":"13-17-Augu","author":"MT Ribeiro","year":"2016","unstructured":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). \u201cWhy should I trust you?\u201d Explaining the predictions of any classifier. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu, 1135\u20131144. https:\/\/doi.org\/10.1145\/2939672.2939778","journal-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"key":"2057_CR37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11491","author":"MT Ribeiro","year":"2018","unstructured":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2018). Anchors: High-precision model-agnostic explanations. Proceedings of the AAAI Conference on Artificial Intelligence. https:\/\/doi.org\/10.1609\/aaai.v32i1.11491","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"8","key":"2057_CR38","doi-asserted-by":"publisher","first-page":"5704","DOI":"10.1287\/MNSC.2021.4190","volume":"68","author":"J Senoner","year":"2021","unstructured":"Senoner, J., Netland, T., & Feuerriegel, S. (2021). Using explainable artificial intelligence to improve process quality: Evidence from semiconductor manufacturing. Management Science, 68(8), 5704\u20135723. https:\/\/doi.org\/10.1287\/MNSC.2021.4190","journal-title":"Management Science"},{"key":"2057_CR47","doi-asserted-by":"publisher","unstructured":"Song, W., Shi, C., Xiao, Z., Duan, Z., Xu, Y., Zhang, M., & Tang, J. (2019). AutoInt: Automatic feature interaction learning via self-attentive neural networks. In Proceedings of the 28th ACM international conference on information and knowledge management (Vol. 10, pp. 11611170). ACM. https:\/\/doi.org\/10.1145\/3357384","DOI":"10.1145\/3357384"},{"issue":"7","key":"2057_CR39","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1007\/S10845-021-01752-9","volume":"32","author":"Y Sun","year":"2021","unstructured":"Sun, Y., Qin, W., Zhuang, Z., & Xu, H. (2021). An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference. Journal of Intelligent Manufacturing, 32(7), 2007\u20132021. https:\/\/doi.org\/10.1007\/S10845-021-01752-9","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2057_CR40","unstructured":"Vinarcik, E. (2002). High Integrity Die Casting Processes. Wiley. Retrieved January 25, 2022, from https:\/\/books.google.com\/books?hl=en&lr=&id=qbHU3al_D-QC&oi=fnd&pg=PR7&dq=high+integrity+die+casting+processes&ots=kSaGalaIvK&sig=cuGD3LYxLuFF0bF4NP5dLprvM-M"},{"issue":"7","key":"2057_CR41","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1007\/S10845-018-1431-X","volume":"31","author":"T Wang","year":"2018","unstructured":"Wang, T., Qiao, M., Zhang, M., Yang, Y., & Snoussi, H. (2018). Data-driven prognostic method based on self-supervised learning approaches for fault detection. Journal of Intelligent Manufacturing, 31(7), 1611\u20131619. https:\/\/doi.org\/10.1007\/S10845-018-1431-X","journal-title":"Journal of Intelligent Manufacturing"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-02057-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-022-02057-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-02057-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T09:05:35Z","timestamp":1706691935000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-022-02057-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,2]]},"references-count":45,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2057"],"URL":"https:\/\/doi.org\/10.1007\/s10845-022-02057-1","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,2]]},"assertion":[{"value":"9 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}