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Since machine and components\u2019 degradations are inevitable, accurately estimating the remaining useful life of them is crucial. We propose an integrated deep learning approach with convolutional neural networks and long short-term memory networks to learn the latent features and estimate remaining useful life value with deep survival model based on the discrete Weibull distribution. We conduct the turbofan engine degradation simulation dataset from Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA to validate our approach. The improved results have proven that our proposed model can capture the degradation trend of a fault and has superior performance under complex conditions compared with existing state-of-the-art methods. Our study provides an efficient feature extraction scheme and offers a promising prediction approach to make better maintenance strategies.<\/jats:p>","DOI":"10.1155\/2020\/8814658","type":"journal-article","created":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T19:23:35Z","timestamp":1607455415000},"page":"1-12","source":"Crossref","is-referenced-by-count":11,"title":["Developing Deep Survival Model for Remaining Useful Life Estimation Based on Convolutional and Long Short-Term Memory Neural Networks"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9967-6072","authenticated-orcid":true,"given":"Chia-Hua","family":"Chu","sequence":"first","affiliation":[{"name":"School of Big Data Management, Soochow University, Taipei City 111, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0902-794X","authenticated-orcid":true,"given":"Chia-Jung","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Big Data Management, Soochow University, Taipei City 111, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6267-9958","authenticated-orcid":true,"given":"Hsiang-Yuan","family":"Yeh","sequence":"additional","affiliation":[{"name":"School of Big Data Management, Soochow University, Taipei City 111, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2005.09.012"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.11.016"},{"article-title":"Deep learning and its applications to machine health monitoring: A surve","year":"2016","author":"R. 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