{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T17:01:53Z","timestamp":1761238913588,"version":"3.44.0"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030859015"},{"type":"electronic","value":"9783030859022"}],"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\/978-3-030-85902-2_15","type":"book-chapter","created":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T03:04:20Z","timestamp":1630465460000},"page":"132-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Digitization of Real-Time Predictive Maintenance for High Speed Machine Equipment"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1179-7740","authenticated-orcid":false,"given":"Rony","family":"Mitra","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3017-0716","authenticated-orcid":false,"given":"Mayank","family":"Shukla","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4420-0077","authenticated-orcid":false,"given":"Adrijit","family":"Goswami","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8564-1402","authenticated-orcid":false,"given":"Manoj Kumar","family":"Tiwari","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1016\/j.ymssp.2005.09.012","volume":"20","author":"AKS Jardine","year":"2006","unstructured":"Jardine, A.K.S., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20, 1483\u20131510 (2006). https:\/\/doi.org\/10.1016\/j.ymssp.2005.09.012","journal-title":"Mech. Syst. Signal Process."},{"doi-asserted-by":"publisher","unstructured":"Caesarendra, W., Tjahjowidodo, T.: A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing. Machines 5 (2017). https:\/\/doi.org\/10.3390\/machines5040021","key":"15_CR2","DOI":"10.3390\/machines5040021"},{"key":"15_CR3","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1016\/S0043-1648(03)00098-X","volume":"255","author":"Z Peng","year":"2003","unstructured":"Peng, Z., Kessissoglou, N.: An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis. Wear 255, 1221\u20131232 (2003). https:\/\/doi.org\/10.1016\/S0043-1648(03)00098-X","journal-title":"Wear"},{"doi-asserted-by":"crossref","unstructured":"Hu, Q., Si, X.S., Zhang, Q.H., Qin, A.S.: A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests (2020)","key":"15_CR4","DOI":"10.1016\/j.ymssp.2019.106609"},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.1109\/TII.2020.2998102","volume":"17","author":"T Xiahou","year":"2021","unstructured":"Xiahou, T., Zeng, Z., Liu, Y.: Remaining useful life prediction by fusing expert knowledge and condition monitoring information. IEEE Trans. Ind. Inform. 17, 2653\u20132663 (2021). https:\/\/doi.org\/10.1109\/TII.2020.2998102","journal-title":"IEEE Trans. Ind. Inform."},{"doi-asserted-by":"crossref","unstructured":"Xia, T., Song, Y., Zheng, Y., Pan, E., Xi, L.: An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation (2020)","key":"15_CR6","DOI":"10.1016\/j.compind.2019.103182"},{"doi-asserted-by":"crossref","unstructured":"Motahari-Nezhad, M., Jafari, S.M.: Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing (2021)","key":"15_CR7","DOI":"10.1016\/j.eswa.2020.114391"},{"doi-asserted-by":"publisher","unstructured":"Jung, D., Winslett, M.: Vibration analysis for IoT enabled predictive maintenance (2017). https:\/\/doi.org\/10.1109\/ICDE.2017.170","key":"15_CR8","DOI":"10.1109\/ICDE.2017.170"},{"key":"15_CR9","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1145\/1710115.1710126","volume":"37","author":"O Tickoo","year":"2010","unstructured":"Tickoo, O., Iyer, R., Illikkal, R., Newell, D.: Modeling virtual machine performance: challenges and approaches. Perform. Eval. Rev. 37, 55\u201360 (2010). https:\/\/doi.org\/10.1145\/1710115.1710126","journal-title":"Perform. Eval. Rev."},{"doi-asserted-by":"crossref","unstructured":"Jemielniak, K.: Commercial tool condition monitoring systems, pp. 711\u2013721 (1999)","key":"15_CR10","DOI":"10.1007\/s001700050123"},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1115\/1.2748475","volume":"129","author":"X Chiementin","year":"2007","unstructured":"Chiementin, X., Bolaers, F., Dron, J.P.: Early detection of fatigue damage on rolling element bearings using adapted wavelet. J. Vib. Acoust. Trans. ASME. 129, 495\u2013506 (2007). https:\/\/doi.org\/10.1115\/1.2748475","journal-title":"J. Vib. Acoust. Trans. ASME."},{"key":"15_CR12","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1016\/j.measurement.2011.02.017","volume":"44","author":"J Huang","year":"2011","unstructured":"Huang, J., Hu, X., Yang, F.: Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker. Meas. J. Int. Meas. Confed. 44, 1018\u20131027 (2011). https:\/\/doi.org\/10.1016\/j.measurement.2011.02.017","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.rcim.2015.11.006","volume":"43","author":"H Fernando","year":"2017","unstructured":"Fernando, H., Surgenor, B.: An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine. Robot. Comput. Integr. Manuf. 43, 79\u201388 (2017). https:\/\/doi.org\/10.1016\/j.rcim.2015.11.006","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"15_CR14","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neucom.2018.05.002","volume":"313","author":"X Yan","year":"2018","unstructured":"Yan, X., Jia, M.: A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing 313, 47\u201364 (2018). https:\/\/doi.org\/10.1016\/j.neucom.2018.05.002","journal-title":"Neurocomputing"},{"key":"15_CR15","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.measurement.2019.01.020","volume":"137","author":"J Saari","year":"2019","unstructured":"Saari, J., Str\u00f6mbergsson, D., Lundberg, J., Thomson, A.: Detection and identification of windmill bearing faults using a one-class support vector machine (SVM). Meas. J. Int. Meas. Confed. 137, 287\u2013301 (2019). https:\/\/doi.org\/10.1016\/j.measurement.2019.01.020","journal-title":"Meas. J. Int. Meas. Confed."}],"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\/978-3-030-85902-2_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T22:05:58Z","timestamp":1756677958000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-85902-2_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030859015","9783030859022"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-85902-2_15","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)"}}]}}