摘要
提出了一种基于小波去噪和主元分析的故障检测和诊断方法。该方法利用小波分析先对正常工况下的数据进行处理,然后运用T2统计、Q统计方法,结合主元得分图和变量贡献图对一模型进行了仿真分析,结果表明,该方法是有效的。
A method of fault detection and diagnosis based on wavelet de-noise and principal component analysis is proposed. The data collected from the normal industry condition are processed by means of the wavelet analysis. The fault detection and diagnosis simulation to a model is performed by means of statistical method like Hotelling T^e and Q. The principal component scores charts and variables contribution charts are used to undertake fault diagnosis. Simulation results show that it is fairly effective.
出处
《石油化工自动化》
CAS
2006年第1期41-44,共4页
Automation in Petro-chemical Industry
关键词
主元分析
小波去噪
故障检测
故障诊断
principal component analysis（PCA）
wavelet de-noise
fault detections fault diagnosis
