{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:41:01Z","timestamp":1743129661147,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031138317"},{"type":"electronic","value":"9783031138324"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-13832-4_58","type":"book-chapter","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T11:32:22Z","timestamp":1660563142000},"page":"709-718","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Remaining Useful Life Prediction Based on Improved LSTM Hybrid Attention Neural Network"],"prefix":"10.1007","author":[{"given":"Mang","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yunyi","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Pengjiang","family":"Qian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"key":"58_CR1","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.apm.2017.10.026","volume":"55","author":"X Huang","year":"2018","unstructured":"Huang, X., Li, Y., Zhang, Y., et al.: A new direct second-order reliability analysis method. Appl. Math. Model. 55, 68\u201380 (2018)","journal-title":"Appl. Math. Model."},{"key":"58_CR2","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.isatra.2018.12.025","volume":"95","author":"W Zhang","year":"2019","unstructured":"Zhang, W., Li, X., Ding, Q.: Deep residual learning-based fault diagnosis method for rotating machinery[J]. ISA Trans. 95, 295\u2013305 (2019)","journal-title":"ISA Trans."},{"key":"58_CR3","doi-asserted-by":"crossref","unstructured":"Bird, J., Wu, X., Patnaik, P., et al.: A framework of prognosis and health management: a multidisciplinary approach. In: Turbo Expo: Power for Land, Sea, and Air, vol. 4790, pp. 177\u2013186 (2007)","DOI":"10.1115\/GT2007-27953"},{"issue":"24","key":"58_CR4","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1016\/j.ifacol.2018.09.705","volume":"51","author":"R Schacht-Rodr\u00edguez","year":"2018","unstructured":"Schacht-Rodr\u00edguez, R., Ponsart, J.C., Garcia-Beltran, C.D., et al.: Prognosis & health management for the prediction of UAV flight endurance. IFAC-PapersOnLine 51(24), 983\u2013990 (2018)","journal-title":"IFAC-PapersOnLine"},{"key":"58_CR5","doi-asserted-by":"publisher","first-page":"109254","DOI":"10.1016\/j.rser.2019.109254","volume":"113","author":"Y Li","year":"2019","unstructured":"Li, Y., Liu, K., Foley, A.M., et al.: Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review. Renew. Sustain. Energy Rev. 113, 109254 (2019)","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"2","key":"58_CR6","first-page":"223","volume":"28","author":"SL Sun","year":"2013","unstructured":"Sun, S.L., Liu, L.F.: Optimal linear estimation for ARMA signals with stochastic multiple packet dropouts. Control Decis. 28(2), 223\u2013228 (2013)","journal-title":"Control Decis."},{"issue":"2","key":"58_CR7","doi-asserted-by":"publisher","first-page":"520","DOI":"10.3390\/en7020520","volume":"7","author":"S Tang","year":"2014","unstructured":"Tang, S., Yu, C., Wang, X., et al.: Remaining useful life prediction of lithium-ion batteries based on the wiener process with measurement error. Energies 7(2), 520\u2013547 (2014)","journal-title":"Energies"},{"issue":"3","key":"58_CR8","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1016\/j.ejor.2018.02.033","volume":"271","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Si, X., Hu, C., et al.: Degradation data analysis and remaining useful life estimation: a review on Wiener-process-based methods. Eur. J. Oper. Res. 271(3), 775\u2013796 (2018)","journal-title":"Eur. J. Oper. Res."},{"issue":"1","key":"58_CR9","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007469218079","volume":"32","author":"S Fine","year":"1998","unstructured":"Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden Markov model: analysis and applications. Mach. Learn. 32(1), 41\u201362 (1998)","journal-title":"Mach. Learn."},{"issue":"05","key":"58_CR10","doi-asserted-by":"publisher","first-page":"2051007","DOI":"10.1142\/S0218001420510076","volume":"34","author":"Z Lin","year":"2020","unstructured":"Lin, Z., Gao, H., Zhang, E., et al.: Diamond-coated mechanical seal remaining useful life prediction based on convolution neural network. Int. J. Pattern Recogn. Artif. Intell. 34(05), 2051007 (2020)","journal-title":"Int. J. Pattern Recogn. Artif. Intell."},{"key":"58_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., et al.: A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing 240, 98\u2013109 (2017)","journal-title":"Neurocomputing"},{"key":"58_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ress.2017.11.021","volume":"172","author":"X Li","year":"2018","unstructured":"Li, X., Ding, Q., Sun, J.Q.: Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab. Eng. Syst. Saf. 172, 1\u201311 (2018)","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"8","key":"58_CR13","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":"58_CR14","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.procs.2018.01.106","volume":"127","author":"AZ Hinchi","year":"2018","unstructured":"Hinchi, A.Z., Tkiouat, M.: Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network. Proc. Comput. Sci. 127, 123\u2013132 (2018)","journal-title":"Proc. Comput. Sci."},{"key":"58_CR15","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., et al.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 International Conference on Prognostics and Health Management, pp. 1\u20139. IEEE (2008)","DOI":"10.1109\/PHM.2008.4711414"},{"issue":"1","key":"58_CR16","first-page":"152","volume":"36","author":"X Li","year":"2021","unstructured":"Li, X., Yao, C.L., Fan, F.L., et al.: Recurrent neural networks based paraphrase identification model combined with attention mechanism. Control Decis. 36(1), 152\u2013158 (2021)","journal-title":"Control Decis."},{"issue":"8","key":"58_CR17","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","volume":"108","author":"SY Shih","year":"2019","unstructured":"Shih, S.Y., Sun, F.K., Lee, H.: Temporal pattern attention for multivariate time series forecasting. Mach. Learn. 108(8), 1421\u20131441 (2019)","journal-title":"Mach. Learn."},{"key":"58_CR18","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., et al.: Damage propagation modeling for aircraft engine\nrun-to-failure simulation. In: 2008 international conference on prognostics and health\nmanagement, pp. 1\u20139. IEEE (2008)","DOI":"10.1109\/PHM.2008.4711414"},{"issue":"1","key":"58_CR19","first-page":"440","volume":"2","author":"AV Devadoss","year":"2013","unstructured":"Devadoss, A.V., Ligori, T.A.A.: Forecasting of stock prices using multi layer perceptron. Int. J. Comput. Algorithm 2(1), 440\u2013449 (2013)","journal-title":"Int. J. Comput. Algorithm"},{"issue":"11","key":"58_CR20","doi-asserted-by":"publisher","first-page":"943","DOI":"10.3390\/rs8110943","volume":"8","author":"Y Gu","year":"2016","unstructured":"Gu, Y., Wylie, B.K., Boyte, S.P., et al.: An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data. Remote Sens. 8(11), 943 (2016)","journal-title":"Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Methodologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13832-4_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:12:49Z","timestamp":1710339169000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13832-4_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138317","9783031138324"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13832-4_58","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2022\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"IC-ICC-CN","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"449","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":"209","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":"47% - 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":"2.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}