{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:05:20Z","timestamp":1775264720442,"version":"3.50.1"},"reference-count":23,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"content-version":"vor","delay-in-days":353,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005024","name":"Beijing Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["ZZ-2019-65"],"award-info":[{"award-number":["ZZ-2019-65"]}],"id":[{"id":"10.13039\/501100005024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1308300"],"award-info":[{"award-number":["2018YFB1308300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62103056"],"award-info":[{"award-number":["62103056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short\u2010term memory (MSDCNN\u2010LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of \u201c0\u201d to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN\u2010LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero\u2010propulsion system simulation (C\u2010MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.<\/jats:p>","DOI":"10.1155\/2021\/2122655","type":"journal-article","created":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T04:05:25Z","timestamp":1640059525000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Prediction Method for the RUL of Equipment for Missing Data"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7683-2776","authenticated-orcid":false,"given":"Chen","family":"Wenbai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0455-2171","authenticated-orcid":false,"given":"Liu","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Weizhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liu","family":"Huixiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9194-3236","authenticated-orcid":false,"given":"Chen","family":"Qili","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1228-2757","authenticated-orcid":false,"given":"Wu","family":"Peiliang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,12,20]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mfglet.2020.04.011"},{"key":"e_1_2_9_2_2","article-title":"Deep convolutional generative adversarial network based missing data generation method and its application in remaining useful life prediction","volume":"42","author":"Zhang S. 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ZhaoP. andLiX.-L. Deep convolutional neural network based regression approach for estimation of remaining useful life International conference on database systems for advanced applications April 2016 Dallas TX USA Springer 214\u2013228 https:\/\/doi.org\/10.1007\/978-3-319-32025-0_14 2-s2.0-84962468883.","DOI":"10.1007\/978-3-319-32025-0_14"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.11.021"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106113"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2021.03.012"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1646\/1\/012122"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1051\/jnwpu\/20213920407"},{"key":"e_1_2_9_21_2","volume-title":"Generative Adversarial Networks","author":"Goodfellow I. J.","year":"2014"},{"key":"e_1_2_9_22_2","unstructured":"YoonJ. JordonJ. andSchaarM. 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