Fault diagnosis for condensers based on fuzzy support vector machine
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Fault diagnosis for condensers based on fuzzy support vector machine
Thermal Power GenerationVol. 44, Issue 6, Pages: 98-101(2015)
作者机构:
燕京理工学院机电工程学院
作者简介:
基金信息:
DOI:
CLC:TH165.3;TP18
Published:2015
稿件说明:
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GONG Huanchun. Fault diagnosis for condensers based on fuzzy support vector machine[J]. Thermal power generation, 2015, 44(6): 98-101.
DOI:
GONG Huanchun. Fault diagnosis for condensers based on fuzzy support vector machine[J]. Thermal power generation, 2015, 44(6): 98-101.DOI:
Fault diagnosis for condensers based on fuzzy support vector machine
摘要
分析了凝汽器工作过程及故障机理
建立了凝汽器典型故障集、征兆集及典型故障特征向量集合以及基于模糊支持向量机(SVM)的故障诊断模型
用实例计算证明了其有效性。经与神经网络方法对比后发现
在小样本情况下
采用SVM方法的计算结果更优越
推广能力更强
而且效率更高。该方法为建立智能化的凝汽器状态监控和故障诊断提供了一种新的途径
具有广泛的应用价值。
Abstract
The working process and fault mechanism of condensers were analyzed.Moreover
the typical fault set
symptom set and typical fault feature vectors were established.A fault diagnosis model was built on the basis of fuzzy support vector machine(SVM)method.Furthermore
specific sample calculations were carried out to demonstrate the effectness of this method.Compared with the neural network model
the SVM method is superior in terms of calculation results
generalization ability and efficiency
under the condition of small quantity samples.When a relatively small number of diagnosis samples are involved
this SVM method will provide a new approach for creating an intelligent system with highly practical value in condensers condition monitoring and fault diagnosis.