The Fault Diagnosis of Blower Ventilator Based-on Multi-class Support Vector Machines

被引：2

作者：

Wu Xing-wei ^{[1
]}

机构：

[1] Shenyang Inst Engn, Energy & Power Engn Dept, Shenyang 110136, Peoples R China

来源：

2012 INTERNATIONAL CONFERENCE ON FUTURE ELECTRICAL POWER AND ENERGY SYSTEM, PT B | 2012年 / 17卷

关键词：

Support Vector Machines; ventilator; faults diagnosis;

D O I：

10.1016/j.egypro.2012.02.226

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Blower ventilators are one of the main rotating equipment in the thermal power plant, supervising and forecasting the faults of the operating ventilator can significantly improve the safety and economy of ventilator as well as guarantee the normal operation of blower. In this paper, the basic principle of faults diagnosis and advantages of DAGSVM are analyzed, the knowledge library of ventilator operating faults is established and trained based-on DAGSVM. Taking a large-scale boiler blower as an example, the DAGSVM model is used to diagnose the actual operating faults, the result shown that DAGSVM can diagnose the common faults of ventilator effectively. Forecasting the ventilator operating circumstance by this method can improve the safety and economy of ventilator operating. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Hainan University.

引用

页码：1193 / 1200

页数：8