本文關(guān)鍵詞：基于改進(jìn)NPE算法的間歇過程故障診斷研究　出處：《蘭州理工大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 間歇過程 故障診斷 鄰域保持嵌入算法(NPE) SPA-MKNPE GNPE-LICA TGNPE TDNPE

【摘要】：隨著工業(yè)過程朝著智能化、大規(guī)模和集成化方向發(fā)展,生產(chǎn)過程變的越來越復(fù)雜。復(fù)雜系統(tǒng)容易受到外部環(huán)境的干擾和自身設(shè)備的老化而發(fā)生各種故障,要實(shí)現(xiàn)工業(yè)過程自動(dòng)化來提高生產(chǎn)效率,就要求控制系統(tǒng)處于穩(wěn)定狀態(tài),需要對(duì)生產(chǎn)過程進(jìn)行實(shí)時(shí)監(jiān)控。間歇生產(chǎn)方式由于自身的優(yōu)點(diǎn)在實(shí)際應(yīng)用中越來越廣,間歇過程的產(chǎn)品是批次輸出,一旦生產(chǎn)過程的某個(gè)時(shí)刻或某個(gè)變量發(fā)生故障,而得不到及時(shí)解決,都會(huì)使整個(gè)批次產(chǎn)品達(dá)不到要求,從而帶來巨大的經(jīng)濟(jì)損失,因此間歇過程的過程監(jiān)控和故障診斷就更為重要。對(duì)于復(fù)雜的間歇過程,我們很難獲得準(zhǔn)確的過程模型,基于數(shù)據(jù)驅(qū)動(dòng)的多元統(tǒng)計(jì)方法,不需要得到過程的精確模型,只需過程的歷史和在線數(shù)據(jù)就能實(shí)現(xiàn)對(duì)過程的監(jiān)控和故障診斷,隨著傳感器技術(shù)的發(fā)展和廣泛應(yīng)用于工業(yè)過程,過程的歷史和時(shí)時(shí)數(shù)據(jù)的獲取變得相對(duì)容易,為基于數(shù)據(jù)驅(qū)動(dòng)的故障診斷的廣泛應(yīng)用提供可能。如何在海量的過程數(shù)據(jù)中充分提取對(duì)過程監(jiān)控有用的特征信息是過程監(jiān)控與故障診斷的關(guān)鍵,相比與主元分析(PCA)算法只關(guān)注過程的全局特征信息,鄰域保持嵌入(NPE)算法從數(shù)據(jù)的局部特征信息出發(fā)來挖過出更多的間歇過程的細(xì)節(jié)特征信息。本文主要針對(duì)間歇生產(chǎn)過程在監(jiān)控和故障診斷過程中存在的問題,對(duì)鄰域保持嵌入算法(NPE)進(jìn)行了改進(jìn),其主要的研究?jī)?nèi)容如下:1.針對(duì)間歇過程的強(qiáng)非線性,傳統(tǒng)方法只是對(duì)數(shù)據(jù)的協(xié)方差矩陣進(jìn)行分解,忽略數(shù)據(jù)高階統(tǒng)計(jì)量信息,導(dǎo)致無(wú)法充分提取非線性過程的有效信息造成診斷效果不佳的問題,結(jié)合核算法和高維統(tǒng)計(jì)量信息提出了一種基于統(tǒng)計(jì)量的多向核鄰域保持嵌入(SPA-MKNPE)算法。該算法首先引入統(tǒng)計(jì)量模式分析(SPA)方法將樣本數(shù)據(jù)投影到統(tǒng)計(jì)量樣本空間中,可以更充分地提取非線性數(shù)據(jù)的高階統(tǒng)計(jì)量信息;然后在統(tǒng)計(jì)量空間中通過核函數(shù)將統(tǒng)計(jì)量樣本映射到高維核空間,用以解決數(shù)據(jù)的非線性;最后在高維核空間中應(yīng)用鄰域保持嵌入算法充分提取數(shù)據(jù)的局部結(jié)構(gòu)來對(duì)間歇過程進(jìn)行監(jiān)控,檢測(cè)到過程故障后用變量貢獻(xiàn)圖法來診斷出故障變量。通過青霉素發(fā)酵過程來驗(yàn)證SPA-MKNPE算法對(duì)強(qiáng)非線性的間歇過程故障診斷的有效性。2.針對(duì)間歇生產(chǎn)過程數(shù)據(jù)很難滿足單一的高斯分布,通常既包含高斯成分又包含非高斯成分的混合多分布,傳統(tǒng)方法在特征提取時(shí)不能兼顧數(shù)據(jù)的全局和局部特征的問題,提出了MGNPE-LICA的故障診斷算法。首先用D檢驗(yàn)法將原始數(shù)據(jù)分成高斯空間和非高斯空間,對(duì)于高斯空間用MGNPE算法在充分提取數(shù)據(jù)的局部結(jié)構(gòu)特征時(shí)兼顧數(shù)據(jù)的全局特征;對(duì)于非高斯空間用MLICA算法在解決非高斯問題的同時(shí)更好地保持?jǐn)?shù)據(jù)的全局和局部信息。再將兩個(gè)空間的監(jiān)控指標(biāo)合成一個(gè)聯(lián)合監(jiān)控指標(biāo)對(duì)過程進(jìn)行監(jiān)控。通過青霉素發(fā)酵過程驗(yàn)證了MGNPE-LICA算法的在解決間歇過程數(shù)據(jù)多分布的有效性。3.針對(duì)間歇過程的三維數(shù)據(jù)在展開成二維的過程中必然會(huì)導(dǎo)致數(shù)據(jù)內(nèi)在結(jié)構(gòu)破壞和全局與局部特征信息在提取過程中無(wú)法兼顧的問題,提出了張量全局-局部鄰域保持嵌入算法(TGNPE),首先用張量分解的方法直接對(duì)三維數(shù)據(jù)進(jìn)行建模,而不對(duì)數(shù)據(jù)進(jìn)行展開,有效地保存了數(shù)據(jù)的內(nèi)部結(jié)構(gòu),再用鄰域保持嵌入算法充分提取數(shù)據(jù)局部結(jié)構(gòu)信息的同時(shí)兼顧數(shù)據(jù)的全局信息,實(shí)現(xiàn)對(duì)數(shù)據(jù)特征信息更加充分地提取,TGNPE算法檢測(cè)到故障后用貢獻(xiàn)圖法診斷出故障變量。通過青霉素發(fā)酵過程驗(yàn)證了TGNPE算法更利于間歇過程數(shù)據(jù)信息的提取,診斷精度更高。4.針對(duì)間歇過程在時(shí)序和空間上的動(dòng)態(tài)特性,提出了基于張量分解的動(dòng)態(tài)鄰域保持嵌入算法(TDNPE)。首先用張量分解的方法把間歇過程數(shù)據(jù)看成二階張量,在張量空間對(duì)數(shù)據(jù)建模避免數(shù)據(jù)展開的向量化過程,導(dǎo)致數(shù)據(jù)內(nèi)部結(jié)構(gòu)被破壞;然后用動(dòng)態(tài)鄰域保持嵌入算法在張量空間提取過程特征信息,同時(shí)考慮空間和時(shí)序上的局部特征,有效解決了過程的動(dòng)態(tài)特性。通過青霉素發(fā)酵過程的仿真實(shí)驗(yàn)驗(yàn)證了TDNPE算法的有效性。
[Abstract]:With the development of industrial process toward intelligent, large-scale and integrated development, the production process becomes more and more complex. The complex system is vulnerable to the interference of the external environment and their own aging equipment and all kinds of faults, to realize the automation of industrial process to improve production efficiency, requires the control system is stable, the need for real-time monitoring of the the production process of batch process. Because of its advantages of more and more widely in practical applications, batch process products are batch output, once a time of the production process or a variable fault, but it is not solved in time, will make the whole batch of products meet the requirements, so as to bring huge economic losses therefore, process monitoring and fault diagnosis of batch process is more important. For the batch process is complicated, it is difficult to obtain accurate process model, based on data driven Multivariate statistical methods, does not need the precise model of the process, only the history and online data can be achieved on the process monitoring and fault diagnosis, along with the development of sensor technology and widely used in the industrial process, the process of acquiring data and history always become easy, may provide for the wide application of data driven fault diagnosis based on the data in the process. How to fully extract the feature information useful for monitoring process is the key process monitoring and fault diagnosis, compared with principal component analysis (PCA) algorithms only focus on the global features of the process of information, neighborhood preserving embedding (NPE) algorithm from the local feature information of data dig out minutiae information for batch process more. This article mainly aims at the existing in the batch process monitoring and fault diagnosis problems in the process of neighborhood preserving embedding algorithm (NPE) Has been improved, and its main research contents are as follows: 1. for strong nonlinear batch process, the traditional approach is to decompose the covariance matrix of the data, ignoring the data information of high order statistics, to fully extract effective information of nonlinear process caused by the problem of poor diagnostic effect, combined with the accounting method and the high dimensional statistics information is proposed a multi kernel neighborhood preserving embedding (SPA-MKNPE) algorithm based on statistics. Firstly, statistic pattern analysis (SPA) method of sample data will be projected to the statistics in the sample space, you can more fully extract the information of high order statistics and nonlinear data; statistics in space by kernel function will be mapped into high statistics dimensional kernel space is used to solve the nonlinear data; finally the application of neighborhood in the high dimensional kernel space is fully extracted according to local node number preserving embedding algorithm Structure to monitor batch process, detect process after fault by using variable contribution plot method to diagnose the fault variables. Through the penicillin fermentation process to validate the SPA-MKNPE algorithm for nonlinear batch process fault diagnosis of the effectiveness of.2. for batch process data is very difficult with single foot Gauss distribution, mixing usually includes Gauss component it contains non Gauss component distribution, traditional methods can not take into account the data of global and local features in feature extraction, we propose a fault diagnosis algorithm of MGNPE-LICA. Firstly, D test was used for the original data into Gauss space and Gauss space, the Gauss space MGNPE algorithm is used to extract the local structure in full feature data when considering the global features of the data; for non Gauss space with MLICA algorithm in solving the problem of non Gauss and better maintain the global and local data The Ministry of information. Then the two space monitoring index of the synthesis of a joint monitoring indicators to monitor the process of penicillin fermentation process. Through the validation of the MGNPE-LICA algorithm in solving the distributed batch process data the validity of.3. for 3D data of batch process in the process of two-dimensional in internal structure will inevitably lead to destruction and global data with the local feature information cannot be taken into account in the process of extraction problem, we propose a tensor global local neighborhood preserving embedding algorithm (TGNPE). The method first uses tensor decomposition model directly on 3D data, data without expansion, effectively preserve the internal structure of the data, and then the neighborhood preserving embedding algorithm fully extracted data locally the structure information of both global information data and realize more fully to extract the data feature information, TGNPE algorithm to detect faults after use The contribution of graph fault diagnosis variable. Through the penicillin fermentation process to verify the TGNPE extraction method is more conducive to the data information of batch process, higher diagnostic accuracy of.4. for dynamic characteristics of the batch process in time and space, and proposes a dynamic neighborhood tensor decomposition keep embedding (TDNPE) algorithm based on tensor decomposition method first uses. The batch process data as two order tensor in the tensor space data to the quantification process of data modeling to avoid, the internal structure of the data is destroyed; and then use dynamic neighborhood preserving embedding algorithm in tensor Space Extraction of feature information, and the local feature space and time, effectively solves the dynamic characteristics of the process. Through the simulation experiment of penicillin fermentation process to verify the effectiveness of the TDNPE algorithm.

【學(xué)位授予單位】：蘭州理工大學(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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