{"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/dynamic-process-fault-prediction-using","title":"Dynamic process fault prediction using canonical variable trend analysis","arxiv_id":null,"date":"2016-04-12","proceeding":"DV 2016 4","authors":["Yongping Hu","Xiaogang Deng","Yuping Cao","Xuemin Tian"],"abstract":"Fault prediction technology is important to avoid \r\nserious process failure. This paper is concerned with the fault \r\nprediction of dynamic industrial process with incipient faults and \r\nproposes a canonical variable trend analysis (CVTA) based fault \r\nprediction method. In the proposed method, canonical variate \r\nanalysis (CVA) algorithm is firstly applied to analyze the process \r\ndynamics and extract the uncorrelated latent features, called \r\ncanonical variables. Furthermore, support vector machine is \r\nadopted to model the relationship between the historical and \r\nfuture values of the canonical variables, which leads to the time \r\nseries prediction model for the canonical variables. Based on the \r\npredicted canonical variables, an overall monitoring statistic is \r\nused to forecast the change of the process status. Simulations on a \r\ncontinuous stirred tank reactor (CSTR) system demonstrate that \r\nthe proposed method can indicate the trend of the incipient faults \r\neffectively.","url_abs":"https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2022&filename=HGSZ202202031&uniplatform=NZKPT&v=_4IVe5p5GCbAa9ik4jXrldbAaclw3GJoASXm7rTBBSMg9taU-KQkAVhDjoBA2Lo5","url_pdf":"https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2022&filename=HGSZ202202031&uniplatform=NZKPT&v=_4IVe5p5GCbAa9ik4jXrldbAaclw3GJoASXm7rTBBSMg9taU-KQkAVhDjoBA2Lo5","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":"dynamic-process-fault-prediction-using","repo_url":"https://github.com/KeepFloyding/knowledge-repository","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}