Early Warning of Power Plant Equipment Based on Massive Real-Time Data Mining Technology

Autor:	Xiao Qing Xiao, Li Kun Zheng, Wei Qiao Song, Kun Feng
Rok vydání:	2014
Předmět:	Real time data mining Engineering Warning system Power station business.industry Remote diagnosis General Medicine Computer security computer.software_genre Safety Equipment Systems engineering Quantitative assessment State (computer science) business computer Power equipment
Zdroj:	Applied Mechanics and Materials. :1487-1490
ISSN:	1662-7482
Popis:	This paper mainly discusses the application of the mass real-time data mining technology in equipment safety state evaluation in the power plant and the realization of the equipment comprehensive quantitative assessment and early warning of potential failure by mining analysis and modeling massive amounts of real-time data the power equipment. In addition to the foundational technology introduced in this paper, the technology is also verified by the application case in the power supply side remote diagnosis center of Guangdong electric institute.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::30170d5a7a715fcc4a4b7cf0a0dca625 https://doi.org/10.4028/www.scientific.net/amm.599-601.1487 Zobrazit plný text záznamu