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  本文關(guān)鍵詞：基于可預(yù)測偏最小二乘算法的復(fù)雜工況過程的監(jiān)控技術(shù)　出處：《上海交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 故障檢測與診斷 可預(yù)測元分析 偏最小二乘 主動學(xué)習(xí) 向量自回歸 故障預(yù)測

【摘要】：隨著信息采集、傳輸、存儲和處理技術(shù)的不斷發(fā)展,工業(yè)過程中有大量反映生產(chǎn)過程和設(shè)備運(yùn)行的數(shù)據(jù)被采集和存儲,如何有效地利用這些離線和在線數(shù)據(jù),提取出能夠反映工業(yè)過程特征的信息,用于工業(yè)過程的監(jiān)控,以此保證設(shè)備安全運(yùn)行,提高生產(chǎn)效率和產(chǎn)品質(zhì)量,成為了目前故障檢測與診斷領(lǐng)域的重點(diǎn)研究內(nèi)容。基于數(shù)據(jù)驅(qū)動的過程監(jiān)控技術(shù)應(yīng)運(yùn)而生,已成為未來過程監(jiān)控技術(shù)的重要發(fā)展方向�？深A(yù)測元分析(Fore CA)作為新興的數(shù)據(jù)降維技術(shù),它能夠找到一個最優(yōu)的轉(zhuǎn)換方式將多變量時間序列分解為一個可預(yù)測空間和一個白噪聲空間。分解得到的可預(yù)測性因子能夠反映過程的本質(zhì)特征,這主要是由于該算法是從時間序列的角度出發(fā),將過程數(shù)據(jù)的序列相關(guān)性考慮在內(nèi),且其將時域特性變換到頻域,在頻域利用信息熵來衡量不確定性,從而保證較好的可預(yù)測性。鑒于Fore CA算法的上述優(yōu)點(diǎn),本文將其引入過程監(jiān)控領(lǐng)域,并將其用于回歸,與偏最小二乘(PLS)算法相結(jié)合,提出一種基于可預(yù)測偏最小二乘(Fore PLS)的故障檢測模型,并利用其回歸預(yù)測性能進(jìn)行多故障診斷,最后將其與時間序列分析方法相結(jié)合實(shí)現(xiàn)對緩變故障的預(yù)測。為探索復(fù)雜工況下的故障檢測、診斷與預(yù)測方法做出了有益的嘗試。下面具體介紹一下本文的主要工作:(1)將Fore CA算法用于回歸并與PLS方法相結(jié)合,提出了可預(yù)測偏最小二乘(Fore PLS)方法。該算法能夠提取出過程數(shù)據(jù)特征空間中與質(zhì)量變量相關(guān)的可預(yù)測性特征。(2)將提出的Fore PLS方法用于故障檢測,構(gòu)建基于Fore PLS的故障檢測模型,并根據(jù)Fore PLS算法的特點(diǎn)構(gòu)造了CUSUM統(tǒng)計(jì)量和SPE統(tǒng)計(jì)量,用來進(jìn)行故障的檢測。(3)提出了基于DFore PLS回歸預(yù)測的多故障診斷方法,為了解決多類分類中的不平衡分類問題,將主動學(xué)習(xí)引入故障診斷領(lǐng)域,有目的地挑選邊界附近最有“信息量”的樣本進(jìn)行訓(xùn)練,避免了冗余樣本對分類器精度的影響,提高了分類器對故障樣本的識別能力,同時也提高了分類器的訓(xùn)練效率。(4)將Fore PLS模型與向量自回歸模型結(jié)合,提出了一種針對緩變故障的基于時間序列的故障預(yù)測方法。能夠有效防止這類緩變故障對系統(tǒng)帶來的損失,同時可以避免頻繁更換部件,提高了生產(chǎn)效率。
[Abstract]:With the continuous development of information collection, transmission, storage and processing technology, a large number of industrial processes reflect the operation of the production process and equipment data acquisition and storage, how to effectively use these offline and online data, extract the feature information can reflect the industrial process, to monitor the industrial process, in order to ensure the safe operation of equipment. Improve the production efficiency and product quality, has become the current field of fault detection and diagnosis. The key research contents emerged process monitoring technology based on data driven, has become an important direction for future development of process control technology. Predictable element analysis (Fore CA) as a new dimensionality reduction technique, it can find an optimal conversion the multivariate time series is decomposed into a predictable space and a white noise space. The decomposed predictability factor can reflect the process of the The character, this is mainly because the algorithm is starting from the perspective of time series, the serial correlation of process data into account, and the time domain is transformed into frequency domain, in the frequency domain using the information entropy to measure the uncertainty, so as to ensure good predictability. In view of the advantages of Fore CA algorithm, the the introduction of process monitoring field, and used regression and partial least squares (PLS) algorithm are combined to propose a prediction based on partial least squares (Fore PLS) fault detection model, and by using the regression prediction performance of multi fault diagnosis, and the time series analysis method combined to predict slow in order to explore the fault. The fault detection under complex condition, diagnosis and prediction method is a beneficial attempt. The main work of this paper introduces in detail below: (1) Fore CA and PLS algorithm for regression A combination method proposed can predict the partial least squares (Fore PLS) method. The algorithm can extract the predictable characteristics associated with quality variables in the feature space of process data. (2) Fore PLS the proposed method is used for fault detection, fault detection module Fore construction based on PLS, and construct CUSUM statistics according to the statistic characteristics of Fore and SPE PLS algorithm, used for fault detection. (3) propose a method for fault diagnosis of DFore based on PLS regression prediction, in order to solve the multi class classification of unbalanced classification problem, the active learning into the fault diagnosis field, to select the most "near the boundary information" the training samples, to avoid the influence on the precision of the classification of the redundant samples, improve the recognition ability of the classifier for fault samples, but also improve the classifier training efficiency. (4) the Fore PLS model and vector auto Based on regression model, a fault prediction method based on time series for slowly varying faults is proposed. It can effectively prevent such slow failures from causing damage to the system, and avoid frequent replacement of components and improve production efficiency.

【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP277

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本文編號：1399128