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

Li Kun Zheng; Kun Feng; Xiao Qing Xiao; Wei Qiao Song

doi:10.4028/www.scientific.net/AMM.599-601.1487

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 599-601Early Warning of Power Plant Equipment Based on...

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

Abstract:

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.

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Periodical:

Applied Mechanics and Materials (Volumes 599-601)

Pages:

1487-1490

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.599-601.1487

Citation:

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Online since:

August 2014

Authors:

Li Kun Zheng*, Kun Feng, Xiao Qing Xiao, Wei Qiao Song

Keywords:

Early Warning of the Fault, Real Time Data Mining, Safety State of Quantitative Assessment

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* - Corresponding Author

References

[1] Liu yun-sheng, Lu yan-sheng, Features of real-time database and its links with the active database, Computer Engineering and Applications, (1993).

Google Scholar

[2] Zhang bao-zhen, The development and application of foreign comprehensive diagnostics, prognostics and health management technology, Computer Measurement and Control, Vol. 16(5), 591-594.

Google Scholar

[3] Wang dao-ping, Zhang yi-zhong．Theory and Method of Intelligent Fault Diagnosis System, Beijing, Metallurgical Industry Press，(2001).

Google Scholar

[4] Xu zhang-sui，Fang li-qing，Wang xi-wu．Fault diagnosis principle and application information, Beijing, Defense Industry Press，(2000).

Google Scholar

[5] Ma xiao-xiao, Intelligent Fault Diagnosis new machine learning theory and its application, Chongqing, Chongqing University，(2002).

Google Scholar

[6] Ma liang-yu，Wang bing-shu，Gao jian-qiang, A new method for the production process slightly intelligent fault diagnosis and early, Chinese Society for Electrical Engineering，2002，22（6）：115~118.

Google Scholar

[7] Zhai yong-jie，Wang dong-feng，Han pu, Fault diagnosis of steam turbine generator based on multi-class support vector machines, Power Engineering，2003, 10, Vol. 23(5): 2694-2698.

Google Scholar

[8] Beijing Ruitai Technology Co., Ltd. ，iEM Technical white paper system，(2011).

Google Scholar