{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:13:43Z","timestamp":1772122423913,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819682942","type":"print"},{"value":"9789819682959","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-8295-9_11","type":"book-chapter","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T17:47:36Z","timestamp":1750355256000},"page":"159-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["VDASI: VAE-Enhanced Degradation-Aware System Identification Using Constrained Latent Spaces"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8491-1908","authenticated-orcid":false,"given":"Saumya","family":"Karunadhika","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1363-8308","authenticated-orcid":false,"given":"Ling","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2948-8770","authenticated-orcid":false,"given":"Bastian","family":"Oetomo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5773-2227","authenticated-orcid":false,"given":"Michele","family":"Discepola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2679-2275","authenticated-orcid":false,"given":"Uwe","family":"Aickelin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jmsy.2019.11.008","volume":"54","author":"L Chen","year":"2020","unstructured":"Chen, L., et al.: Health indicator construction of machinery based on end-to-end trainable convolution recurrent neural networks. J. Manuf. Syst. 54, 1\u201311 (2020)","journal-title":"J. Manuf. Syst."},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.05.020","volume":"457","author":"C Kim","year":"2018","unstructured":"Kim, C., et al.: DeepNAP: deep neural anomaly pre-detection in a semiconductor fab. Inf. Sci. 457, 1\u201311 (2018)","journal-title":"Inf. Sci."},{"key":"11_CR3","unstructured":"Kingma, D.P., Welling, M., et\u00a0al.: Auto-encoding variational bayes (2013)"},{"key":"11_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107106","volume":"151","author":"P Li","year":"2020","unstructured":"Li, P., et al.: A novel scalable method for machine degradation assessment using deep convolutional neural network. Measurement 151, 107106 (2020)","journal-title":"Measurement"},{"key":"11_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107709","volume":"162","author":"MJ de Lima","year":"2021","unstructured":"de Lima, M.J., et al.: HealthMon: an approach for monitoring machines degradation using time-series decomposition, clustering, and metaheuristics. Comput. Ind. Eng. 162, 107709 (2021)","journal-title":"Comput. Ind. Eng."},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Lin, X., et\u00a0al.: Fault diagnosis of aero-engine bearing using a stacked auto-encoder network. In: ITOEC, pp. 545\u2013548. IEEE (2018)","DOI":"10.1109\/ITOEC.2018.8740504"},{"key":"11_CR7","unstructured":"Malhotra, P., et\u00a0al.: Multi-sensor prognostics using an unsupervised health index based on lstm encoder-decoder. arXiv preprint arXiv:1608.06154 (2016)"},{"key":"11_CR8","unstructured":"Mauthe, F., et\u00a0al.: Overview of publicly available degradation data sets for tasks within prognostics and health management. arXiv:2403.13694 (2024)"},{"key":"11_CR9","unstructured":"One year industrial component degradation dataset. https:\/\/www.kaggle.com\/datasets\/inIT-OWL\/one-year-industrial-component-degradation"},{"key":"11_CR10","unstructured":"Preventive to predictive maintenance dataset. https:\/\/www.kaggle.com\/datasets\/prognosticshse\/preventive-to-predicitve-maintenance\/data"},{"key":"11_CR11","unstructured":"Process data for predictive maintenance dataset. https:\/\/www.kaggle.com\/datasets\/boyangs444\/process-data-for-predictive-maintenance"},{"issue":"9","key":"11_CR12","doi-asserted-by":"publisher","first-page":"6438","DOI":"10.1109\/TII.2020.2999442","volume":"17","author":"Y Qin","year":"2020","unstructured":"Qin, Y., et al.: Gated dual attention unit neural networks for remaining useful life prediction of rolling bearings. IEEE Trans. Ind. Inf. 17(9), 6438\u20136447 (2020)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.ymssp.2017.02.003","volume":"93","author":"A Rai","year":"2017","unstructured":"Rai, A., Upadhyay, S.H.: Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering. Mech. Syst. Signal Process. 93, 16\u201329 (2017)","journal-title":"Mech. Syst. Signal Process."},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"She, D., Jia, M.: Health indicator construction of rolling bearings based on deep convolutional neural network considering phase degradation. In: PHM-Paris, pp. 373\u2013378. IEEE (2019)","DOI":"10.1109\/PHM-Paris.2019.00070"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Subramanian, A., et\u00a0al.: Servomotor dataset: modeling health in mechanisms with typically intermittent operation. In: PHM Society Conference, vol. 15, no. 1 (2023)","DOI":"10.36001\/phmconf.2023.v15i1.3580"},{"issue":"4","key":"11_CR16","doi-asserted-by":"publisher","first-page":"2110","DOI":"10.1177\/1475921720963951","volume":"20","author":"F Xu","year":"2021","unstructured":"Xu, F., et al.: Health indicator construction for roller bearing based on an unsupervised deep belief network with a novel sigmoid zero local minimum point model. Struct. Health Monit. 20(4), 2110\u20132123 (2021)","journal-title":"Struct. Health Monit."},{"issue":"8","key":"11_CR17","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3390\/membranes10080171","volume":"10","author":"H Xu","year":"2020","unstructured":"Xu, H., et al.: A simple method to identify the dominant fouling mechanisms during membrane filtration. Membranes 10(8), 171 (2020)","journal-title":"Membranes"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Yang, Z., et\u00a0al.: Automatic extraction of a health indicator from vibrational data by sparse autoencoders. In: ICSRS, pp. 328\u2013332. IEEE (2018)","DOI":"10.1109\/ICSRS.2018.8688720"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Yao, R., et\u00a0al.: Unsupervised anomaly detection using variational auto-encoder based feature extraction. In: ICPHM, pp.\u00a01\u20137. IEEE (2019)","DOI":"10.1109\/ICPHM.2019.8819434"},{"issue":"3","key":"11_CR20","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/s13042-022-01657-w","volume":"14","author":"C Zhang","year":"2023","unstructured":"Zhang, C., et al.: VESC: a new variational autoencoder based model for anomaly detection. Int. J. Mach. Learn. Cybern. 14(3), 683\u2013696 (2023). https:\/\/doi.org\/10.1007\/s13042-022-01657-w","journal-title":"Int. J. Mach. Learn. Cybern."}],"container-title":["Lecture Notes in Computer Science","Data Science: Foundations and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8295-9_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T17:47:39Z","timestamp":1750355259000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8295-9_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819682942","9789819682959"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8295-9_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"20 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}