摘要
根据故障信号特征和小波包变换多尺度分解性质选取小波包分解层次,得到能正确地反映风机的运行状态的特征向量;参照特征向量的组成方法,提出并构建基于小波包分析的韶钢4号风机典型故障特征表。对待检信号选用db10小波进行6层小波包分解,利用待检状态的特征向量与典型故障特征表,通过模糊模式识别方法进行风机故障诊断。
Wavelet packet decomposition levels were selected according to the characteristics of fault signal and wavelet packet transforming multiscale decomposition property, and the feature vector was obtained that can be used to reflect the running status of the fan. According to the feature vector composition method, wavelet packet analysis method was used to deal with the fault diagnosis of 4th sintering fan in Shaogang Steel Group, and a feature table of typical fault was built. Db10 wavelet was selected for detecting signals to carry out six layers wavelet packet decomposition. The fuzzy identification method was used to make fault diagnosis by applying the characteristic vector with state to be tested and typical fault feature table.
出处
《风机技术》
2010年第3期49-51,55,共4页
Chinese Journal of Turbomachinery
关键词
风机小波包分析
故障诊断
fan
wavelet packet analysis
fault diagnosis
