{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T22:40:09Z","timestamp":1756680009354,"version":"3.44.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030859053"},{"type":"electronic","value":"9783030859060"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-85906-0_65","type":"book-chapter","created":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T14:03:00Z","timestamp":1630504980000},"page":"599-608","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Machine Learning Based Health Indicator Construction in Implementing Predictive Maintenance: A Real World Industrial Application from Manufacturing"],"prefix":"10.1007","author":[{"given":"Harshad","family":"Kurrewar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4858-4386","authenticated-orcid":false,"given":"Ebru Turanouglu","family":"Bekar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8519-0736","authenticated-orcid":false,"given":"Anders","family":"Skoogh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Per","family":"Nyqvist","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Bokrantz, J., Skoogh, A., Berlin, C., Wuest, T., Stahre, J.: Smart maintenance: A research agenda for industrial maintenance management. Int. J. Prod. Econ. 224, 107547 (2020)","key":"65_CR1","DOI":"10.1016\/j.ijpe.2019.107547"},{"doi-asserted-by":"publisher","unstructured":"May, G., et al.: Predictive maintenance platform based on integrated strategies for increased operating life of factories. In: Moon, I., Lee, G.M., Park, J., Kiritsis, D., Cieminski, G.V. (eds.) APMS 2018. IFIP AICT, vol. 536, pp. 279\u2013287. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99707-0","key":"65_CR2","DOI":"10.1007\/978-3-319-99707-0"},{"issue":"11","key":"65_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1115\/1.4047856","volume":"142","author":"J Lee","year":"2020","unstructured":"Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., Feng, J.: Intelligent maintenance systems and predictive manufacturing. ASME. J. Manuf. Sci. Eng. 142(11), 1\u201323 (2020)","journal-title":"ASME. J. Manuf. Sci. Eng."},{"issue":"1","key":"65_CR4","first-page":"23","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest, T., Weimer, D., Irgens, C., Thoben, K.D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23\u201345 (2016)","journal-title":"Prod. Manuf. Res."},{"doi-asserted-by":"crossref","unstructured":"Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.D.P., Basto, J.P., Alcal\u00e1, S.G.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019)","key":"65_CR5","DOI":"10.1016\/j.cie.2019.106024"},{"key":"65_CR6","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.jmsy.2020.07.008","volume":"56","author":"JJM Jimenez","year":"2020","unstructured":"Jimenez, J.J.M., Schwartz, S., Vingerhoeds, R., Grabot, B., Sala\u00fcn, M.: Towards multi-model approaches to predictive maintenance: a systematic literature survey on diagnostics and prognostics. J. Manuf. Syst. 56, 539\u2013557 (2020)","journal-title":"J. Manuf. Syst."},{"doi-asserted-by":"publisher","unstructured":"Lughofer, E., Mouchaweh, S. M.: Predictive Maintenance in Dynamic Systems, 1st edn. Springer, Switzerland (2019). https:\/\/doi.org\/10.1007\/978-3-030-05645-2","key":"65_CR7","DOI":"10.1007\/978-3-030-05645-2"},{"issue":"8","key":"65_CR8","doi-asserted-by":"publisher","first-page":"2425","DOI":"10.3390\/s20082425","volume":"20","author":"JF Olesen","year":"2020","unstructured":"Olesen, J.F., Shaker, H.R.: Predictive maintenance for pump systems and thermal power plants: state-of-the-art review, trends and challenges. Sensors 20(8), 2425 (2020)","journal-title":"Sensors"},{"doi-asserted-by":"crossref","unstructured":"Zhai, S., Gehring, B., Reinhart, G.: Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning. J. Manuf. Syst. (2021)","key":"65_CR9","DOI":"10.1016\/j.jmsy.2021.02.006"},{"doi-asserted-by":"crossref","unstructured":"\u00c7\u0131nar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., Safael, B.: Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability 12(19), 8211 (2020)","key":"65_CR10","DOI":"10.3390\/su12198211"},{"issue":"31","key":"65_CR11","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.neucom.2017.02.045","volume":"240","author":"L Guo","year":"2017","unstructured":"Guo, L., Li, N., Jia, F., Lei, Y., Lin, J.: A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing 240(31), 98\u2013109 (2017)","journal-title":"Neurocomputing"},{"doi-asserted-by":"publisher","unstructured":"Fink, O.: Data-driven intelligent predictive maintenance of industrial assets. In: Smith A. (eds) Women in Industrial and Systems Engineering. Women in Engineering and Science, pp. 589\u2013605. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11866-2_25","key":"65_CR12","DOI":"10.1007\/978-3-030-11866-2_25"},{"issue":"1","key":"65_CR13","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1016\/j.ymssp.2017.11.016","volume":"104","author":"Y Lei","year":"2018","unstructured":"Lei, Y., Li, N., Guo, L., Li, N., Yan, T., Lin, J.: Machinery health prognostics: a systematic review from data acquisition to RUL prediction. Mech. Syst. Sig. Process. 104(1), 799\u2013834 (2018)","journal-title":"Mech. Syst. Sig. Process."},{"doi-asserted-by":"crossref","unstructured":"Ning, Y., Wang, G., Yu, J., Jiang, H.: A feature selection algorithm based on variable correlation and time correlation for predicting remaining useful life of equipment using RNN. In: Proceedings of the 2018 Condition Monitoring and Diagnosis (CMD), pp. 1\u20136. IEEE, Australia (2018)","key":"65_CR14","DOI":"10.1109\/CMD.2018.8535843"},{"issue":"3","key":"65_CR15","first-page":"86","volume":"4","author":"T Gittler","year":"2020","unstructured":"Gittler, T., Scholze, S., Rupenyan, A., Wegener, K.: Machine tool component health identification with unsupervised learning. J. Manuf. Mater. Process. 4(3), 86 (2020)","journal-title":"J. Manuf. Mater. Process."},{"key":"65_CR16","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.procs.2021.01.199","volume":"181","author":"C Schr\u00f6er","year":"2021","unstructured":"Schr\u00f6er, C., Kruse, F., G\u00f3mez, J.M.: A systematic literature review on applying CRISP-DM process model. Procedia Comput. Sci. 181, 526\u2013534 (2021)","journal-title":"Procedia Comput. Sci."},{"key":"65_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.apacoust.2017.01.005","volume":"120","author":"L Saidi","year":"2020","unstructured":"Saidi, L., Ali, J.B., Bechhoefer, E., Benbouzid, M.: Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Appl. Acoust. 120, 1\u20138 (2020)","journal-title":"Appl. Acoust."},{"doi-asserted-by":"crossref","unstructured":"Bekar, E. T., Nyqvist, P., Skoogh, A.: An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study. Adv. Mech. Eng. 12(5) (2020)","key":"65_CR18","DOI":"10.1177\/1687814020919207"},{"issue":"3","key":"65_CR19","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1504\/IJAPR.2016.079733","volume":"3","author":"A Tharwat","year":"2016","unstructured":"Tharwat, A.: Principal component analysis-a tutorial. Int. J. Appl. Pattern Recognit. 3(3), 197\u2013240 (2016)","journal-title":"Int. J. Appl. Pattern Recognit."},{"key":"65_CR20","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/s11265-019-01491-4","volume":"92","author":"V Atamuradov","year":"2020","unstructured":"Atamuradov, V., Medjaher, K., Camci, F., Zerhouni, N., Dersin, P., Lamoureux, B.: Machine health indicator construction framework for failure diagnostics and prognostics. J. Sign. Process. Syst. 92, 591\u2013609 (2020)","journal-title":"J. Sign. Process. Syst."},{"issue":"12","key":"65_CR21","doi-asserted-by":"publisher","first-page":"2270","DOI":"10.1016\/j.patcog.2005.01.012","volume":"38","author":"A Jain","year":"2005","unstructured":"Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recognit. 38(12), 2270\u20132285 (2005)","journal-title":"Pattern Recognit."}],"container-title":["IFIP Advances in Information and Communication Technology","Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-85906-0_65","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T22:04:27Z","timestamp":1756677867000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-85906-0_65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030859053","9783030859060"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-85906-0_65","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"31 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APMS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Advances in Production Management Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nantes","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apms2021","order":10,"name":"conference_id","label":"Conference ID","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":"Conftool","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"529","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":"378","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":"71% - 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":"3.2","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":"3.3","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)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}