Turbo-generator vibration fault prediction using gray prediction model

Guizhong Tang, Guangming Zhang, Jianming Gong, Tianpeng Qiang, Guo Li

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Research on turbo-generator fault prediction is one of theory bases for its fault self-recovery, however, the lack of fault samples and the incompletion of fault information make it full difficulties. This paper presents an efficient method for turbo-generator vibration fault prediction in which the new model of gray forecasting with first-order fitting parameter is established. On the basis of the first-order exponent flatness operation for the energies in different frequency bands extracted by wavelet packet decomposition, a new turbo-generator fault gray prediction model is established to reconstruct feature vectors consisting of the energies in different frequency bands. And then, five typical turbo-generator vibration faults are identified by using SVM. Experimental results showed that the proposed method could effectively and efficiently forecast delitescent faults and typical fault genres for the turbo-generator vibration.

源语言英语
主期刊名Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08
8536-8541
页数6
DOI
出版状态已出版 - 2008
活动7th World Congress on Intelligent Control and Automation, WCICA'08 - Chongqing, 中国
期限: 25 6月 200827 6月 2008

出版系列

姓名Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

会议

会议7th World Congress on Intelligent Control and Automation, WCICA'08
国家/地区中国
Chongqing
时期25/06/0827/06/08

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