{"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/temporal-convolutional-memory-networks-for","title":"Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery","arxiv_id":"1810.05644","date":"2018-10-12","proceeding":null,"authors":["Lahiru Jayasinghe","Tharaka Samarasinghe","Chau Yuen","Jenny Chen Ni Low","Shuzhi Sam Ge"],"abstract":"Accurately estimating the remaining useful life (RUL) of industrial machinery\nis beneficial in many real-world applications. Estimation techniques have\nmainly utilized linear models or neural network based approaches with a focus\non short term time dependencies. This paper, introduces a system model that\nincorporates temporal convolutions with both long term and short term time\ndependencies. The proposed network learns salient features and complex temporal\nvariations in sensor values, and predicts the RUL. A data augmentation method\nis used for increased accuracy. The proposed method is compared with several\nstate-of-the-art algorithms on publicly available datasets. It demonstrates\npromising results, with superior results for datasets obtained from complex\nenvironments.","url_abs":"http://arxiv.org/abs/1810.05644v2","url_pdf":"http://arxiv.org/pdf/1810.05644v2.pdf","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":"temporal-convolutional-memory-networks-for","repo_url":"https://github.com/LahiruJayasinghe/RUL-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"}],"methods":[],"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}