本文選題：主元分析(PCA) + 小波變換�。� 參考：《南京師范大學(xué)》2011年碩士論文

【摘要】：由于現(xiàn)代化大生產(chǎn)的迅速發(fā)展以及科學(xué)技術(shù)的迅速進(jìn)步,現(xiàn)代工業(yè)系統(tǒng)的結(jié)構(gòu)越來越復(fù)雜,投資越來越大,自動化水平越來越高,因此系統(tǒng)過程的安全性和可靠性就顯得特別重要�；谥髟治龅亩嘣y(tǒng)計(jì)故障檢測與診斷技術(shù)是目前過程自動化和控制領(lǐng)域的研究熱點(diǎn)問題之一 本文首先介紹了故障診斷方法、多元統(tǒng)計(jì)方法和TE過程模型,研究了基于主元分析方法的故障檢測技術(shù)以及利用貢獻(xiàn)圖進(jìn)行故障診斷的基本方法。針對主元分析方法在進(jìn)行故障檢測與診斷時(shí)存在的不足,將主元分析與小波變換、RBF神經(jīng)網(wǎng)絡(luò)等方法相結(jié)合,分別提出了基于小波去噪主元分析的故障檢測與診斷方法,基于魯棒主元分析的故障檢測與診斷方法以及基于RBF神經(jīng)網(wǎng)絡(luò)的非線性主元分析的故障檢測方法,將動態(tài)主元分析用于故障檢測與診斷,提出基于動態(tài)主元分析的故障檢測與診斷方法,以TE模型為對象進(jìn)行了仿真研究和仿真結(jié)果分析。主要研究工作為： 1、研究了主元分析的基本原理,簡單介紹了田納西-伊斯曼(TE)模型,給出了基于主元分析的故障檢測與診斷算法流程,通過TE模型進(jìn)行仿真研究,根據(jù)SPE和T2統(tǒng)計(jì)量的變化來判斷是否發(fā)生故障,根據(jù)變量對統(tǒng)計(jì)量的貢獻(xiàn)來判斷故障變量、識別故障源,實(shí)現(xiàn)故障的檢測與診斷。 2、針對傳統(tǒng)主元分析在處理含噪數(shù)據(jù)時(shí)的不足,研究了小波變換的基本原理,結(jié)合基于主元分析方法的故障檢測與診斷方法,提出基于小波去噪主元分析方法的故障檢測與診斷方法,TE模型的仿真研究表明該方法能有效地減少主元個(gè)數(shù),降低誤報(bào)率,提高了故障檢測與診斷的效果。 3、針對傳統(tǒng)主元分析方法要求建模數(shù)據(jù)的噪聲服從正態(tài)分布,提出了一種基于魯棒主元分析的故障檢測與診斷方法。該方法使用簡單的加權(quán)方差-協(xié)方差的估計(jì)值代替?zhèn)鹘y(tǒng)的協(xié)方差,在此基礎(chǔ)上建立主元模型構(gòu)造SPE和T2統(tǒng)計(jì)量來檢測過程故障并根據(jù)變量對統(tǒng)計(jì)量的貢獻(xiàn)來判斷故障變量、識別故障源,TE模型的仿真研究表明了該方法優(yōu)于傳統(tǒng)的主元分析方法。 4、針對傳統(tǒng)主元分析方法不能有效地監(jiān)視動態(tài)多元過程,提出了一種基于動態(tài)主元分析(DPCA)的故障檢測方法,根據(jù)測量數(shù)據(jù)建立動態(tài)主元模型,在該模型基礎(chǔ)上利用SPE和T2統(tǒng)計(jì)量進(jìn)行故障檢測,以TE模型為對象進(jìn)行了仿真研究,證實(shí)了基于動態(tài)主元分析進(jìn)行故障檢測時(shí)考慮時(shí)序相關(guān)性是由于傳統(tǒng)主元分析。 5、針對傳統(tǒng)主元分析方法的非線性局限性,將RBF神經(jīng)網(wǎng)絡(luò)與非線性主元建模相結(jié)合,提出基于RBF神經(jīng)網(wǎng)絡(luò)的非線性主元分析故障檢測方法,利用RBF神經(jīng)網(wǎng)絡(luò)訓(xùn)練學(xué)習(xí)得到非線性主元的負(fù)載矩陣從而建立主元模型,仿真研究表明該方法在對非線性系統(tǒng)進(jìn)行故障檢測時(shí)優(yōu)于傳統(tǒng)的主元分析方法。
[Abstract]:As a result of the rapid development of modern mass production and the rapid progress of science and technology, the structure of modern industrial systems is becoming more and more complex, the investment is increasing, and the level of automation is becoming higher and higher. Therefore, the safety and reliability of the system process is particularly important. Multivariate statistical fault detection and diagnosis based on principal component analysis (PCA) is one of the hot issues in the field of process automation and control. This paper first introduces the fault diagnosis method, multivariate statistical method and te process model. The fault detection technology based on principal component analysis (PCA) and the basic method of fault diagnosis based on contribution diagram are studied. Aiming at the shortcomings of principal component analysis in fault detection and diagnosis, combining principal component analysis with wavelet transform RBF neural network, a fault detection and diagnosis method based on wavelet denoising principal component analysis is proposed. The method of fault detection and diagnosis based on robust principal component analysis and nonlinear principal component analysis based on RBF neural network is presented. Dynamic principal component analysis is applied to fault detection and diagnosis. A fault detection and diagnosis method based on dynamic principal component analysis (DPCA) is proposed. The te model is taken as the object of simulation and the simulation results are analyzed. The main research work is as follows: 1. The basic principle of principal component analysis (PCA) is studied, and the Tennessee Eastman (TET) model is briefly introduced. The flow of fault detection and diagnosis algorithm based on PCA is presented, and the simulation is carried out through te model. According to the changes of SPE and T2 statistics to determine whether the fault occurs, according to the contribution of variables to the statistics to judge the fault variables, identify the fault source, 2. Aiming at the deficiency of traditional principal component analysis in dealing with noisy data, the basic principle of wavelet transform is studied, and the fault detection and diagnosis method based on principal component analysis is combined. A fault detection and diagnosis method based on wavelet denoising principal component analysis (PCA) is proposed. The simulation results show that the method can effectively reduce the number of principal components and the false alarm rate. The effect of fault detection and diagnosis is improved. 3. In view of the noise distribution of modeling data required by traditional principal component analysis method, a fault detection and diagnosis method based on robust principal component analysis is proposed. In this method, a simple weighted variance-covariance estimate is used to replace the traditional covariance. On this basis, the principal component model is constructed to construct SPE and T2 statistics to detect process faults and to judge the fault variables according to the contribution of variables to the statistics. The simulation study of identifying fault source te model shows that this method is superior to the traditional principal component analysis method. 4. The traditional principal component analysis method can not effectively monitor the dynamic multivariate process. A fault detection method based on dynamic principal component analysis (DPCA) is proposed. Based on the measured data, a dynamic principal component model is established. On the basis of this model, SPE and T2 statistics are used for fault detection. The te model is used as an object of simulation. It is proved that the consideration of temporal correlation in fault detection based on dynamic principal component analysis is due to traditional principal component analysis. 5. Aiming at the nonlinear limitation of traditional principal component analysis method, RBF neural network is combined with nonlinear principal component modeling. A nonlinear principal component analysis (NPCA) fault detection method based on RBF neural network is proposed. The load matrix of nonlinear principal component is obtained by training RBF neural network and the principal component model is established. Simulation results show that this method is superior to the traditional principal component analysis method in fault detection of nonlinear systems.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 張媛媛;多尺度自適應(yīng)PCA及其在過程監(jiān)測中的應(yīng)用研究[D];北京化工大學(xué);2012年

2 周瑜;氣固流化床結(jié)片監(jiān)測系統(tǒng)設(shè)計(jì)及算法研究[D];北京化工大學(xué);2012年

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本文編號：2039733