计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
18期
240-245,250
,共7页
最小绝对收缩和选择算子(lasso)%稀疏主元分析%状态监控%田纳西伊斯特曼(TE)过程
最小絕對收縮和選擇算子(lasso)%稀疏主元分析%狀態鑑控%田納西伊斯特曼(TE)過程
최소절대수축화선택산자(lasso)%희소주원분석%상태감공%전납서이사특만(TE)과정
least absolute shrinkage and selection operator(lasso)%Sparse Principal Component Analysis(SPCA)%state monitoring%Tennessee Eastman(TE)processes
主元分析（principal component analysis）是一种多元统计技术，在过程监控和故障诊断中具有广泛的应用。针对过程监控中数据量大的特点，提出一种稀疏主元分析（sparse principal component analysis）方法，通过引入lasso约束函数，构建稀疏主元分析的框架，将PCA降维问题转化为回归最优化问题，从而求解得到稀疏化的主元，并提高了主元模型的抗干扰能力。由于稀疏后主元相关的数据量减少，利用数据建立过程监控模型，减少了计算量，并缩短了计算时间，进而提高了监控的实时性。利用田纳西伊斯特曼过程（TE processes）进行实验仿真，并与传统的主元分析方法进行对比研究。结果表明，新提出的稀疏主元分析方法在计算效率和监控实时性上均优于传统的主元分析方法。
主元分析（principal component analysis）是一種多元統計技術，在過程鑑控和故障診斷中具有廣汎的應用。針對過程鑑控中數據量大的特點，提齣一種稀疏主元分析（sparse principal component analysis）方法，通過引入lasso約束函數，構建稀疏主元分析的框架，將PCA降維問題轉化為迴歸最優化問題，從而求解得到稀疏化的主元，併提高瞭主元模型的抗榦擾能力。由于稀疏後主元相關的數據量減少，利用數據建立過程鑑控模型，減少瞭計算量，併縮短瞭計算時間，進而提高瞭鑑控的實時性。利用田納西伊斯特曼過程（TE processes）進行實驗倣真，併與傳統的主元分析方法進行對比研究。結果錶明，新提齣的稀疏主元分析方法在計算效率和鑑控實時性上均優于傳統的主元分析方法。
주원분석（principal component analysis）시일충다원통계기술，재과정감공화고장진단중구유엄범적응용。침대과정감공중수거량대적특점，제출일충희소주원분석（sparse principal component analysis）방법，통과인입lasso약속함수，구건희소주원분석적광가，장PCA강유문제전화위회귀최우화문제，종이구해득도희소화적주원，병제고료주원모형적항간우능력。유우희소후주원상관적수거량감소，이용수거건립과정감공모형，감소료계산량，병축단료계산시간，진이제고료감공적실시성。이용전납서이사특만과정（TE processes）진행실험방진，병여전통적주원분석방법진행대비연구。결과표명，신제출적희소주원분석방법재계산효솔화감공실시성상균우우전통적주원분석방법。
Principal Component Analysis(PCA)is a multivariate statistical technique, with a range of applications in data processing and dimensionality reduction. Over the past two decades, PCA method has also been widely applied to various kinds of industrial processes for process monitoring and fault diagnosis with some successes. Due to the increasing vol-umes of data, process monitoring methods which are based on PCA approaches suffer many limitations, such as great cal-culation loads and poor real-time performance. In this paper, a new method called Sparse Principal Component Analysis (SPCA)is developed in process monitoring, using the lasso(least absolute shrinkage and selection operator)to produce modified principal components with sparse loadings. And the SPCA can be formulated as a regression-type optimization function to achieve the main elements of choice. Furthermore, the fault detection is then performed by a detection index using model parameters, and the sparse principal component analysis is used in the Tennessee Eastman process(TE pro-cesses)monitoring for simulations. Compared with the traditional principal component analysis method, this SPCA approach builds a model based on the sparse modeling data. Therefore it can reduce the amount of calculations and improve the real time performance. As the SPCA model is applied to simulate with real data, the results show that it has better effective-ness in TE processes.
