本文選題：微小故障　切入點(diǎn)：主元分析　出處：《控制理論與應(yīng)用》2014年01期

【摘要】：主元分析(principal component analysis,PCA)是一種有效的數(shù)據(jù)分析方法,在故障診斷與狀態(tài)監(jiān)測(cè)方面已得到廣泛應(yīng)用.多元指數(shù)加權(quán)移動(dòng)平均 主元分析(multivariate exponentially weighted moving average principal component analysis,MEWMA PCA)方法用于解決PCA不能有效檢出微小故障的問題.本文深入研究了MEWMA PCA中EWMA影響主元分析進(jìn)行故障檢測(cè)的機(jī)制,導(dǎo)出了MEWMA PCA可檢出微小故障的原因.本文確定了MEWMA PCA中遺忘因子λ、單傳感器故障幅值和遲延時(shí)間三者的關(guān)系,并進(jìn)行了數(shù)值仿真和火電廠磨煤機(jī)組運(yùn)行狀態(tài)的仿真實(shí)驗(yàn).實(shí)驗(yàn)結(jié)果驗(yàn)證了MEWMA PCA中EWMA提高PCA的監(jiān)測(cè)性能的機(jī)制,并給出了根據(jù)系統(tǒng)實(shí)際要求來選取合適的遺忘因子值,從而在規(guī)定的時(shí)間內(nèi)檢出微小故障的實(shí)例.
[Abstract]:Principal component analysis (PCA) is an effective method for data analysis. It has been widely used in fault diagnosis and condition monitoring. Multivariate exponentially weighted moving average principal component analysis method is used to solve the problem that PCA can not effectively detect small faults. The mechanism of EWMA influencing principal component analysis (PCA) for fault detection in MEWMA PCA is studied. The reason why MEWMA PCA can detect minor faults is derived. The relationship among forgetting factor 位, single sensor fault amplitude and delay time in MEWMA PCA is determined. The numerical simulation and the simulation experiment on the operation state of coal mill unit in thermal power plant are carried out. The experimental results verify the mechanism of improving the monitoring performance of PCA by EWMA in MEWMA PCA, and the appropriate forgetting factor value is given according to the actual requirements of the system. Thus in the specified time to detect the small failure of the example.
【作者單位】： 華北電力大學(xué)新能源電力系統(tǒng)國(guó)家重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃“973”計(jì)劃資助項(xiàng)目(2012CB215203) 國(guó)家自然科學(xué)基金重點(diǎn)資助項(xiàng)目(51036002) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目
【分類號(hào)】：TM621

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本文編號(hào)：1680734