{"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/recurrent-auto-encoder-model-for-large-scale","title":"Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal Analysis","arxiv_id":"1807.03710","date":"2018-07-10","proceeding":null,"authors":["Timothy Wong","Zhiyuan Luo"],"abstract":"Recurrent auto-encoder model summarises sequential data through an encoder\nstructure into a fixed-length vector and then reconstructs the original\nsequence through the decoder structure. The summarised vector can be used to\nrepresent time series features. In this paper, we propose relaxing the\ndimensionality of the decoder output so that it performs partial\nreconstruction. The fixed-length vector therefore represents features in the\nselected dimensions only. In addition, we propose using rolling fixed window\napproach to generate training samples from unbounded time series data. The\nchange of time series features over time can be summarised as a smooth\ntrajectory path. The fixed-length vectors are further analysed using additional\nvisualisation and unsupervised clustering techniques. The proposed method can\nbe applied in large-scale industrial processes for sensors signal analysis\npurpose, where clusters of the vector representations can reflect the operating\nstates of the industrial system.","url_abs":"http://arxiv.org/abs/1807.03710v1","url_pdf":"http://arxiv.org/pdf/1807.03710v1.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":"recurrent-auto-encoder-model-for-large-scale","repo_url":"https://github.com/lifesailor/data-driven-predictive-maintenance","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}