现代电子技术2017，Vol.40Issue(7)：120-123,4.DOI:10.16652/j.issn.1004-373x.2017.07.032
基于LS-SVM的一次风机振动在线监测及故障预警
LS-SVM based online monitoring and fault warning method for primary air fan vibration
欧阳刚1
作者信息
- 1. 晋能电力集团有限公司 嘉节燃气热电分公司设备管理部,山西 太原 030032
- 折叠
摘要
Abstract
Since it is difficult to construct the accurate equipment model due to the complex operation condition of the heat-engine plant's primary air fan and the strong coupling property of its multi-state variable,the intelligent data mining method is applied to the warning and diagnosis of the air fan equipment. The typical operation characteristic of the air fan is analyzed to propose the primary air fan vibration state estimation and fault warning method based on least-square support vector machine(LS-SVM). In combination with the historical operation data of the first unit's 1# primary air fan in Shanxi Hequ Power Plant,the Matlab is used to verify and analyze the method. The study results indicate that the method has high estimation accuracy,can timely identify the abnormal vibration of the operating primary air fan,is suitable for the fault diagnosis of the heat-engine plant's auxiliary equipment,and has a certain engineering application value.关键词
一次风机/在线监测/最小二乘支持向量机(LS-SVM)/故障预警Key words
primary air fan/online monitoring/least-square support vector machine/fault warning分类
信息技术与安全科学引用本文复制引用
欧阳刚..基于LS-SVM的一次风机振动在线监测及故障预警[J].现代电子技术,2017,40(7):120-123,4.
