{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:20:36Z","timestamp":1766578836115,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981779"},{"type":"electronic","value":"9789819981786"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8178-6_30","type":"book-chapter","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T10:02:54Z","timestamp":1701252174000},"page":"390-401","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Remaining Useful Life Prediction of Control Moment Gyro in Orbiting Spacecraft Based on Variational Autoencoder"],"prefix":"10.1007","author":[{"given":"Tao","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dechang","family":"Pi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"30_CR1","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.actaastro.2020.03.054","volume":"173","author":"D Higashiyama","year":"2020","unstructured":"Higashiyama, D., Shoji, Y., Satoh, S., et al.: Attitude control for spacecraft using pyramid-type variable-speed control moment gyros. Acta Astronaut. 173, 252\u2013265 (2020)","journal-title":"Acta Astronaut."},{"issue":"3","key":"30_CR2","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."},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Peng, Y., Pan, X., Wang, S., et al.: An aero-engine RUL prediction method based on VAE-GAN. In: 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 953\u2013957. IEEE (2021)","DOI":"10.1109\/CSCWD49262.2021.9437836"},{"issue":"4","key":"30_CR4","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.1109\/TIM.2019.2917735","volume":"69","author":"W Mao","year":"2019","unstructured":"Mao, W., He, J., Zuo, M.J.: Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning. IEEE Trans. Instrum. Meas. 69(4), 1594\u20131608 (2019)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"5","key":"30_CR5","doi-asserted-by":"publisher","first-page":"474","DOI":"10.3390\/aerospace10050474","volume":"10","author":"G Youness","year":"2023","unstructured":"Youness, G., Aalah, A.: An explainable artificial intelligence approach for remaining useful life prediction. Aerospace 10(5), 474 (2023)","journal-title":"Aerospace"},{"issue":"1","key":"30_CR6","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/TR.2018.2882682","volume":"69","author":"B Wang","year":"2018","unstructured":"Wang, B., Lei, Y., Li, N., et al.: A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Trans. Reliab. 69(1), 401\u2013412 (2018)","journal-title":"IEEE Trans. Reliab."},{"issue":"3","key":"30_CR7","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1109\/TR.2016.2570568","volume":"65","author":"Y Lei","year":"2016","unstructured":"Lei, Y., Li, N., Gontarz, S., et al.: A model-based method for remaining useful life prediction of machinery. IEEE Trans. Reliab. 65(3), 1314\u20131326 (2016)","journal-title":"IEEE Trans. Reliab."},{"key":"30_CR8","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":"3","key":"30_CR9","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1109\/JSYST.2019.2905565","volume":"13","author":"W Zhang","year":"2019","unstructured":"Zhang, W., Yang, D., Wang, H.: Data-driven methods for predictive maintenance of industrial equipment: a survey. IEEE Syst. J. 13(3), 2213\u20132227 (2019)","journal-title":"IEEE Syst. J."},{"key":"30_CR10","first-page":"100286","volume":"27","author":"A Polenghi","year":"2022","unstructured":"Polenghi, A., Roda, I., Macchi, M., et al.: Ontology-augmented prognostics and health management for shopfloor-synchronised joint maintenance and production management decisions. J. Ind. Inf. Integr. 27, 100286 (2022)","journal-title":"J. Ind. Inf. Integr."},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Sharma, A.K., Punj, P., Kumar, N., et al.: Lifetime prediction of a hydraulic pump using ARIMA model. Arab. J. Sci. Eng. 1\u201313 (2023)","DOI":"10.1007\/s13369-023-07976-6"},{"issue":"6","key":"30_CR12","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.1109\/TII.2017.2684821","volume":"13","author":"Q Zhai","year":"2017","unstructured":"Zhai, Q., Ye, Z.S.: RUL prediction of deteriorating products using an adaptive wiener process model. IEEE Trans. Industr. Inf. 13(6), 2911\u20132921 (2017)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"1","key":"30_CR13","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3390\/en11010028","volume":"11","author":"Z Chen","year":"2017","unstructured":"Chen, Z., Cao, S., Mao, Z.: Remaining useful life estimation of aircraft engines using a modified similarity and supporting vector machine (SVM) approach. Energies 11(1), 28 (2017)","journal-title":"Energies"},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Wu, D., Jennings, C., Terpenny, J., et al.: Cloud-based machine learning for predictive analytics: tool wear prediction in milling. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 2062\u20132069. IEEE (2016)","DOI":"10.1109\/BigData.2016.7840831"},{"key":"30_CR15","doi-asserted-by":"publisher","first-page":"108353","DOI":"10.1016\/j.ress.2022.108353","volume":"222","author":"N Costa","year":"2022","unstructured":"Costa, N., S\u00e1nchez, L.: Variational encoding approach for interpretable assessment of remaining useful life estimation. Reliab. Eng. Syst. Saf. 222, 108353 (2022)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"30_CR16","doi-asserted-by":"publisher","first-page":"106113","DOI":"10.1016\/j.asoc.2020.106113","volume":"89","author":"H Li","year":"2020","unstructured":"Li, H., Zhao, W., Zhang, Y., et al.: Remaining useful life prediction using multi-scale deep convolutional neural network. Appl. Soft Comput. 89, 106113 (2020)","journal-title":"Appl. Soft Comput."},{"issue":"5","key":"30_CR17","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1002\/qre.2651","volume":"36","author":"C Su","year":"2020","unstructured":"Su, C., Li, L., Wen, Z.: Remaining useful life prediction via a variational autoencoder and a time-window-based sequence neural network. Qual. Reliab. Eng. Int. 36(5), 1639\u20131656 (2020)","journal-title":"Qual. Reliab. Eng. Int."},{"issue":"1","key":"30_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-65070-5","volume":"10","author":"J Yan","year":"2020","unstructured":"Yan, J., Mu, L., Wang, L., et al.: Temporal convolutional networks for the advance prediction of ENSO. Sci. Rep. 10(1), 1\u201315 (2020)","journal-title":"Sci. Rep."},{"issue":"1","key":"30_CR19","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., et al.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Networks Learn. Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"30_CR20","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.cam.2018.07.008","volume":"346","author":"C Ordonez","year":"2019","unstructured":"Ordonez, C., Lasheras, F.S., Roca-Pardinas, J., et al.: A hybrid ARIMA\u2013SVM model for the study of the remaining useful life of aircraft engines. J. Comput. Appl. Math. 346, 184\u2013191 (2019)","journal-title":"J. Comput. Appl. Math."}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8178-6_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:32:23Z","timestamp":1709829143000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8178-6_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9789819981779","9789819981786"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8178-6_30","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,30]]},"assertion":[{"value":"30 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}