{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-stacked-dcnn-to-predict-the-rul-of-a","title":"A stacked DCNN to predict the RUL of a turbofan engine","arxiv_id":null,"date":"2021-11-24","proceeding":"Annual conference pf the PHM society 2021 11","authors":["David Solís-Martín","Juan Galán-Páez","Joaquín Borrego-Díaz"],"abstract":"This paper presents the data-driven techniques and methodologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based on two Deep Convolutional Neural Networks (DCNN) stacked in two levels. The first DCNN is used to extract a low-dimensional feature vector using the normalized raw data as input. The second DCNN ingests a list of vectors taken from the former DCNN and estimates the RUL. Model selection was carried out by means of Bayesian optimization using a repeated random subsampling validation approach. The proposed methodology was ranked in the third place of the 2021 PHM Conference Data Challenge.","url_abs":"http://papers.phmsociety.org/index.php/phmconf/article/view/3110","url_pdf":"http://papers.phmsociety.org/index.php/phmconf/article/download/3110/1838","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-stacked-dcnn-to-predict-the-rul-of-a","repo_url":"https://github.com/datrikintelligence/stacked-dcnn-rul-phm21","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"model-selection","task_name":"Model Selection"},{"task_slug":"remaining-useful-lifetime-estimation","task_name":"Remaining Useful Lifetime Estimation"}],"methods":[{"method_slug":"dcnn","method_name":"DCNN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}