{"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/fault-detection-engine-in-intelligent","title":"Fault Detection Engine in Intelligent Predictive Analytics Platform for DCIM","arxiv_id":"1610.04872","date":"2016-10-16","proceeding":null,"authors":["Bodhisattwa Prasad Majumder","Ayan Sengupta","Sajal jain","Parikshit Bhaduri"],"abstract":"With the advancement of huge data generation and data handling capability,\nMachine Learning and Probabilistic modelling enables an immense opportunity to\nemploy predictive analytics platform in high security critical industries\nnamely data centers, electricity grids, utilities, airport etc. where downtime\nminimization is one of the primary objectives. This paper proposes a novel,\ncomplete architecture of an intelligent predictive analytics platform, Fault\nEngine, for huge device network connected with electrical/information flow.\nThree unique modules, here proposed, seamlessly integrate with available\ntechnology stack of data handling and connect with middleware to produce online\nintelligent prediction in critical failure scenarios. The Markov Failure module\npredicts the severity of a failure along with survival probability of a device\nat any given instances. The Root Cause Analysis model indicates probable\ndevices as potential root cause employing Bayesian probability assignment and\ntopological sort. Finally, a community detection algorithm produces correlated\nclusters of device in terms of failure probability which will further narrow\ndown the search space of finding route cause. The whole Engine has been tested\nwith different size of network with simulated failure environments and shows\nits potential to be scalable in real-time implementation.","url_abs":"http://arxiv.org/abs/1610.04872v1","url_pdf":"http://arxiv.org/pdf/1610.04872v1.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":"fault-detection-engine-in-intelligent","repo_url":"https://github.com/majumderb/TheFaultEngine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"fault-detection","task_name":"Fault Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}