發(fā)布時(shí)間：2018-04-08 20:50

  本文選題：啤酒發(fā)酵　切入點(diǎn)：過程監(jiān)控　出處：《哈爾濱理工大學(xué)》2016年碩士論文

【摘要】：有效的過程監(jiān)控是工業(yè)生產(chǎn)過程中安全生產(chǎn)的重要保證,而且在改善產(chǎn)品質(zhì)量和經(jīng)濟(jì)效益方面起到了重要作用。統(tǒng)計(jì)過程監(jiān)控是以多元統(tǒng)計(jì)為理論基礎(chǔ)的一種基于數(shù)據(jù)驅(qū)動(dòng)的方法,通過對(duì)工業(yè)過程監(jiān)控?cái)?shù)據(jù)的處理和分析,獲得工業(yè)過程的運(yùn)行情況,在線檢測(cè)和診斷過程中的異常狀況,根據(jù)異常情況作出相應(yīng)對(duì)策而指導(dǎo)整個(gè)系統(tǒng)運(yùn)行、保證生產(chǎn)效率。本文結(jié)合間歇生產(chǎn)過程特點(diǎn),對(duì)基于Fisher判別分析的過程監(jiān)測(cè)方法進(jìn)行了不同程度的發(fā)展和改進(jìn)。主要研究?jī)?nèi)容及工作如下:1.對(duì)于復(fù)雜的間歇過程,在實(shí)現(xiàn)故障診斷過程中很可能碰見奇異矩陣的現(xiàn)象。為了解決這種奇異矩陣的問題,本文提出了一種基于奇異值分解的改進(jìn)核Fisher判別分析算法,本文在Fisher判別分析方法的故障檢測(cè)基礎(chǔ)上,首先利用核函數(shù)將原始數(shù)據(jù)從原始空間映射到高維空間,然后利用奇異值分解的方法,將處理后的數(shù)據(jù)投影到一個(gè)分解后的非奇異正交矩陣中,最后利用Fisher判別分析算法來實(shí)現(xiàn)過程監(jiān)測(cè)與故障診斷。2.工業(yè)過程數(shù)據(jù)往往來自多個(gè)數(shù)據(jù)源或異構(gòu)的數(shù)據(jù)集,基于采用單個(gè)核函數(shù)的形式處理類似數(shù)據(jù)的效果不是很理想,本文基于多核學(xué)習(xí)的理論,采用多個(gè)基核組合形式,構(gòu)成組合核函數(shù)的形式,與傳統(tǒng)的Fisher判別分析相結(jié)合,提出一種組合核Fisher判別分析算法,通過啤酒發(fā)酵實(shí)驗(yàn)驗(yàn)證了算法的有效性。3.本文根據(jù)Fisher判別分析(有監(jiān)督全局算法)的不足,利用核方法理論,提出了一種能夠挖掘樣本數(shù)據(jù)的全局歐氏分布結(jié)構(gòu)和局部流行分布結(jié)構(gòu)的核局部Fisher判別分析故障診斷算法,該算法充分利用局部保持投影與Fisher判別分析的優(yōu)點(diǎn),對(duì)于采樣數(shù)據(jù)進(jìn)行完全的信息挖掘,通過啤酒發(fā)酵過程,驗(yàn)證了該算法的優(yōu)越性。
[Abstract]:Effective process monitoring is an important guarantee of production safety in industrial production, and plays an important role in improving product quality and economic benefits.Statistical process monitoring is a data-driven method based on multivariate statistics. Through the processing and analysis of industrial process monitoring data, the operation of industrial process, on-line detection and diagnosis of abnormal conditions in the process can be obtained.According to the abnormal situation to make corresponding countermeasures to guide the operation of the whole system, to ensure production efficiency.Based on the characteristics of batch production process, the process monitoring method based on Fisher discriminant analysis has been developed and improved in different degrees.The main contents and work are as follows: 1.For complex intermittent processes, singular matrices are likely to be encountered in the process of fault diagnosis.In order to solve the problem of singular matrix, an improved kernel Fisher discriminant analysis algorithm based on singular value decomposition is proposed in this paper.Firstly, the original data is mapped from the original space to the high-dimensional space by kernel function, and then the processed data is projected into a decomposed nonsingular orthogonal matrix by using the singular value decomposition method.Finally, Fisher discriminant analysis algorithm is used to realize process monitoring and fault diagnosis.Industrial process data often come from multiple data sources or heterogeneous data sets. The effect of processing similar data based on single kernel function is not ideal.Combined with the traditional Fisher discriminant analysis, a combined kernel Fisher discriminant analysis algorithm is proposed. The effectiveness of the algorithm is verified by beer fermentation experiments.In this paper, according to the deficiency of Fisher discriminant analysis (supervised global algorithm), a kernel local Fisher discriminant analysis fault diagnosis algorithm based on global Euclidean distribution structure and local popular distribution structure is proposed by using kernel method theory.The algorithm makes full use of the advantages of local preserving projection and Fisher discriminant analysis, and makes complete information mining for the sampling data. The superiority of the algorithm is verified by the beer fermentation process.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP277

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本文編號(hào)：1723285