本文選題：魯棒　切入點(diǎn)：PPLS算法　出處：《江南大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,工業(yè)生產(chǎn)中安全事故頻發(fā),為了減少財(cái)產(chǎn)損失和人員傷亡,保證生產(chǎn)的安全平穩(wěn)運(yùn)行和產(chǎn)品質(zhì)量,可靠準(zhǔn)確的過程監(jiān)控至關(guān)重要。現(xiàn)代生產(chǎn)過程中保存了大量的數(shù)據(jù),給基于數(shù)據(jù)驅(qū)動的統(tǒng)計(jì)監(jiān)控方法的應(yīng)用和發(fā)展提供了條件和基礎(chǔ)。在傳統(tǒng)過程監(jiān)控方法中,通常假設(shè)樣本數(shù)據(jù)服從正態(tài)分布,且樣本的采樣率一致。但是,實(shí)際變量數(shù)據(jù)分布復(fù)雜并且采樣周期多樣,給過程的監(jiān)控帶來了困難。本文針對實(shí)際工業(yè)中離群值,多采樣率,動態(tài)特性等實(shí)際問題,對概率偏最小二乘(Probabilistic Partial Least Squares,PPLS)模型進(jìn)行推廣,提出了PPLS魯棒建模方法,并將其應(yīng)用到過程監(jiān)控中,主要研究內(nèi)容如下:1、針對工業(yè)過程中離群值問題,分析PPLS模型的不足,提出一種魯棒PPLS方法,用拖尾更寬的T分布代替高斯分布,通過調(diào)整自由度參數(shù),使模型對含離群點(diǎn)數(shù)據(jù)的擬合效果更好。更進(jìn)一步,將魯棒PPLS引入過程監(jiān)控中,構(gòu)建GT2和GSPE兩個監(jiān)控指標(biāo),通過監(jiān)控主元和殘差兩個子空間,判斷系統(tǒng)運(yùn)行狀況。PPLS和魯棒PPLS在TE過程監(jiān)控的應(yīng)用結(jié)果表明魯棒PPLS不僅能更準(zhǔn)確檢測故障的產(chǎn)生,而且能更有效降低故障的漏報(bào)率。2、針對工業(yè)過程中多采樣率問題,基于半監(jiān)督方法,提出一種半監(jiān)督魯棒PPLS方法,將采樣率不一致的完整數(shù)據(jù)分成少數(shù)標(biāo)記樣本和大量未標(biāo)記樣本,然后分別用這兩種樣本數(shù)一致的數(shù)據(jù)建立魯棒PPLS模型,通過充分挖掘大量未標(biāo)記數(shù)據(jù)中的有用信息來提高模型的準(zhǔn)確性。另一方面,通過建立GT2和GSPE_x和GSPE_y三個監(jiān)控指標(biāo),半監(jiān)督魯棒PPLS完成了對主元空間、過程變量的噪聲空間和質(zhì)量變量的噪聲空間的監(jiān)控。通過對半監(jiān)督魯棒PPLS和下采樣魯棒PPLS在TE過程監(jiān)控應(yīng)用中比較,結(jié)果表明半監(jiān)督魯棒PPLS比降采樣魯棒PPLS效果更好。3、針對實(shí)際工況下過程動態(tài)特性問題,基于狀態(tài)空間方式擴(kuò)展過程數(shù)據(jù)矩陣,提出一種動態(tài)魯棒PPLS的數(shù)據(jù)建模方法。動態(tài)魯棒PPLS不僅考慮了變量之間的關(guān)聯(lián)性,而且還提煉出變量在時間維度上的動態(tài)性,從而能對過程進(jìn)行準(zhǔn)確描述提高模型精度。此外,基于動態(tài)魯棒PPLS,引入GT2和GSPE兩個指標(biāo),通過充分融合過程動態(tài)和靜態(tài)的有用信息實(shí)現(xiàn)對動態(tài)過程準(zhǔn)確及時地監(jiān)控。在TE過程中的應(yīng)用研究,反映了動態(tài)魯棒PPLS能更準(zhǔn)確有效的監(jiān)控過程故障的發(fā)生。
[Abstract]:In recent years, safety accidents occur frequently in industrial production. In order to reduce the loss of property and casualties, ensure the safe and stable operation of production and product quality, reliable and accurate process monitoring is very important. It provides the condition and foundation for the application and development of data-driven statistical monitoring method. In the traditional process monitoring method, it is usually assumed that the sample data is normally distributed, and the sample sampling rate is the same. It is difficult to monitor the process because of the complicated distribution of the actual variable data and the variety of sampling period. This paper aims at the practical problems such as outlier value, multi-sampling rate, dynamic characteristic and so on in the actual industry. The probabilistic Partial Least SquaresPPLS model is generalized, and a robust modeling method of PPLS is proposed and applied to process monitoring. The main research contents are as follows: 1. Aiming at the problem of outliers in industrial process, the deficiency of PPLS model is analyzed. In this paper, a robust PPLS method is proposed, in which Gao Si distribution is replaced by T distribution with a wider tail. By adjusting the parameters of degree of freedom, the model can fit outliers better. Furthermore, robust PPLS is introduced into process monitoring. Two monitoring indexes, GT2 and GSPE, are constructed. By monitoring the principal component and residual subspace, the application results of system operation. PPLS and robust PPLS in te process monitoring show that robust PPLS can not only detect the occurrence of faults more accurately. Moreover, it can reduce the failure rate more effectively. Aiming at the problem of multi-sampling rate in industrial process, a semi-supervised robust PPLS method is proposed based on semi-supervised method. The complete data with inconsistent sampling rate are divided into a few labeled samples and a large number of unlabeled samples, and then the robust PPLS model is established by using the data with the same number of samples, respectively. The accuracy of the model is improved by fully mining useful information from a large amount of unmarked data. On the other hand, by establishing three monitoring indexes, GT2, GSPE_x and GSPE_y, semi-supervised robust PPLS completes the principal component space. The noise space of process variable and the noise space of quality variable are monitored. The comparison of semi-supervised robust PPLS and down-sampling robust PPLS in te process monitoring application is carried out. The results show that semi-supervised robust PPLS is more effective than down-sampling robust PPLS. The process data matrix is extended based on state space to solve the problem of process dynamic characteristics under actual working conditions. A data modeling method for dynamic robust PPLS is proposed. Dynamic robust PPLS not only considers the correlation among variables, but also abstracts the dynamics of variables in time dimension, which can accurately describe the process and improve the accuracy of the model. Based on dynamic robust PPLS, two indexes, GT2 and GSPE, are introduced to realize accurate and timely monitoring of dynamic process by fully integrating dynamic and static useful information. It reflects that dynamic robust PPLS can more accurately and effectively monitor the occurrence of process failures.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：X924;TP277

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