{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:19:08Z","timestamp":1742973548264,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030757618"},{"type":"electronic","value":"9783030757625"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-75762-5_24","type":"book-chapter","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T09:07:43Z","timestamp":1620464863000},"page":"290-301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life"],"prefix":"10.1007","author":[{"given":"Dong","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Chengkun","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Chuanfu","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Zhenghua","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,9]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., Xi, L.: Recent advances in prognostics and health management for advanced manufacturing paradigms. Reliab. Eng. Syst. Safety 178, 255\u2013268 (2018)","DOI":"10.1016\/j.ress.2018.06.021"},{"issue":"3","key":"24_CR2","doi-asserted-by":"publisher","first-page":"2276","DOI":"10.1109\/TIE.2016.2623260","volume":"64","author":"R Khelif","year":"2017","unstructured":"Khelif, R., Chebel-Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., Zerhouni, N.: Direct remaining useful life estimation based on support vector regression. IEEE Trans. Ind. Electron. 64(3), 2276\u20132285 (2017)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"24_CR3","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TII.2017.2723943","volume":"14","author":"K Zhu","year":"2018","unstructured":"Zhu, K., Liu, T.: Online tool wear monitoring via hidden semi-Markov model with dependent durations. IEEE Trans. Ind. Inform. 14, 69\u201378 (2018)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"8","key":"24_CR4","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Zheng, S., Ristovski, K., Farahat, A., Gupta, C.: Long short-term memory network for remaining useful life estimation. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM) (2017)","DOI":"10.1109\/ICPHM.2017.7998311"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Listou Ellefsen, A., Bj\u00f8rlykhaug, E., \u00c6s\u00f8y., Ushakov, S., Zhang, H.: Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture. Reliab. Eng. Syst. Safety 183, 240\u2013251 (2019)","DOI":"10.1016\/j.ress.2018.11.027"},{"key":"24_CR7","doi-asserted-by":"publisher","first-page":"8792","DOI":"10.1109\/TIE.2019.2891463","volume":"66","author":"CG Huang","year":"2019","unstructured":"Huang, C.G., Huang, H.Z., Li, Y.F.: A bidirectional LSTM prognostics method under multiple operational conditions. IEEE Trans. Ind. Electron. 66, 8792\u20138802 (2019)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Wang, Q., et al.: Deep image clustering using convolutional autoencoder embedding with inception-like block. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2356\u20132360 (2018)","DOI":"10.1109\/ICIP.2018.8451641"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Li, X., Ding, Q., Sun, J.Q.: Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab. Eng. Syst. Safety 172, 1\u201311 (2018)","DOI":"10.1016\/j.ress.2017.11.021"},{"issue":"2","key":"24_CR10","first-page":"862","volume":"16","author":"W Long","year":"2019","unstructured":"Long, W., Yan, D., Liang, G.: A new ensemble residual convolutional neural network for remaining useful life estimation. Math. Biosci. En. MBE 16(2), 862\u2013880 (2019)","journal-title":"Math. Biosci. En. MBE"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Hong, C.W., Lee, K., Ko, M.S., Kim, J.K., Oh, K., Hur, K.: Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis. In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) (2020)","DOI":"10.1109\/BigComp48618.2020.00-98"},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"103182","DOI":"10.1016\/j.compind.2019.103182","volume":"115","author":"T Xia","year":"2020","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. Comput. Ind. 115, 103182 (2020)","journal-title":"Comput. Ind."},{"key":"24_CR13","unstructured":"Liu, H., Liu, Z., Jia, W., Lin, X.: Remaining useful life prediction using a novel feature-attention based end-to-end approach. IEEE Trans. Ind. Inform. PP(99), 1 (2020)"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, W., Jin, F., Zhang, G., Zhao, B., Hou, Y.: Aero-engine remaining useful life estimation based on 1-dimensional FCN-LSTM neural networks. In: 2019 Chinese Control Conference (CCC), pp. 4913\u20134918 (2019)","DOI":"10.23919\/ChiCC.2019.8866118"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Al-Dulaimi, A., Zabihi, S., Asif, A., Mohammadi, A.: Hybrid deep neural network model for remaining useful life estimation. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)","DOI":"10.1109\/ICASSP.2019.8683763"},{"key":"24_CR16","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (2014)"},{"key":"24_CR17","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"24_CR18","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.: Adversarial autoencoders. In: ICLR (2016)"},{"key":"24_CR19","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., Eklund, N.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 International Conference on Prognostics and Health Management, pp. 1\u20139 (2008)","DOI":"10.1109\/PHM.2008.4711414"},{"key":"24_CR21","first-page":"1191","volume":"42","author":"X Liu","year":"2017","unstructured":"Liu, X., et al.: Multiple kernel k-means with incomplete kernels. IEEE Trans. Pattern Anal. Machine Intell. 42, 1191\u20131204 (2017)","journal-title":"IEEE Trans. Pattern Anal. Machine Intell."},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Heimes, F.O.: Recurrent neural networks for remaining useful life estimation. In: 2008 International Conference on Prognostics and Health Management (2008)","DOI":"10.1109\/PHM.2008.4711422"},{"issue":"10","key":"24_CR23","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1109\/TNNLS.2016.2582798","volume":"28","author":"C Zhang","year":"2017","unstructured":"Zhang, C., Lim, P., Qin, A.K., Tan, K.C.: Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans. Neural Netw. Learn Syst. 28(10), 2306\u20132318 (2017)","journal-title":"IEEE Trans. Neural Netw. Learn Syst."},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Liao, Y., Zhang, L., Liu, C.: Uncertainty prediction of remaining useful life using long short-term memory network based on bootstrap method. In: 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 1\u20138 (2018)","DOI":"10.1109\/ICPHM.2018.8448804"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75762-5_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:15:57Z","timestamp":1710350157000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75762-5_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030757618","9783030757625"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75762-5_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 May 2021","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2021.org\/","order":11,"name":"conference_url","label":"Conference URL","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"673","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":"157","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":"23% - 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","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":"7","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)"}}]}}