CAJ | 학술논문

针对化工过程的非线性以及过程的动态特征，本文开发出了一种基于最小信息熵损的核局部保留算法(MEL-KLPP)。算法优点：①能够有效提取过程中的信息，建立准确的统计模型②在降维过程中考虑了样本之间的关联信息，所得模型更加符合实际。将算法应用于润滑油重质过程以检验其故障检出能力，结果表明MEL-KLPP 算法的误报率和KLPP相近，低于KPCA，故障检出率(81.30%)高于KLPP(3.25%)和KPCA(69.7%)。将过程收集的数据根据工艺知识进行分块建模后，KLPP算法的故障检出率显著提高，MEL-KLPP检出率变化不大，表明KLPP算法对强噪声的复杂数据并不适用，MEL-KLPP算法对数据质量的要求不高，算法鲁棒性好，具有更广阔的应用前景。
침대화공과정적비선성이급과정적동태특정，본문개발출료일충기우최소신식적손적핵국부보류산법(MEL-KLPP)。산법우점：①능구유효제취과정중적신식，건립준학적통계모형②재강유과정중고필료양본지간적관련신식，소득모형경가부합실제。장산법응용우윤활유중질과정이검험기고장검출능력，결과표명MEL-KLPP 산법적오보솔화KLPP상근，저우KPCA，고장검출솔(81.30%)고우KLPP(3.25%)화KPCA(69.7%)。장과정수집적수거근거공예지식진행분괴건모후，KLPP산법적고장검출솔현저제고，MEL-KLPP검출솔변화불대，표명KLPP산법대강조성적복잡수거병불괄용，MEL-KLPP산법대수거질량적요구불고，산법로봉성호，구유경엄활적응용전경。
A new technique named Kernel Locality Preserving Projection based Minimum Entropy Loss (MET-KLPP) is developed to deal with the complex and nonlinear problem for chemical process monitoring,Kernel function and the minimum entropy loss was introduced to the The traditional methods named Locality Preserving Projection(LPP) at the same time. Comparing with other statistical process monitoring methods, MET-KLPP has two advantages in the process of dimension reduction .First, MET-KLPP method considers both the transition matrix eigenvalue and eigenvector, this can be more effective to reveal the essence of data feature and extract more effective information from the data .Second The relationship between samples are considered, so the model was developed using this method is more conform to the actual process. MET-KLPP was test using industry data from a lubricating replacement process to check its effectiveness,the lubricating including furfural refining process and ketone-benzol dewaxing process, the results are compared with other two methods Kernel Locality Preserving Projection (KLPP) and Kernel Principal Component Analysis (KPCA). Fault alarm rate of them is very similar with KLPP (4.31 %), MEL-KLPP (3.68%) and KPCA (6.40%). The fault detection of MET-KLPP is 81.30%, which is higher than KLPP (3.25%) and KPCA (69.7 %). In order to monitor the process and test the methods better the industry data was divided and modeling separately based on the technology knowledge , the fault detection rate of KLPP increased a lot, with 79.06 % for furfural refining process and 13.01 % for ketone-benzol dewaxing process, while MET-KLPP is almost the same with 81.30%for furfural refining process 6.10%for ketone-benzol dewaxing process, the fault detection rate of KPCA is not change two much but it is more lower than MET-KLPP, this means MET-KLPP have a good robustness and a wide application prospect.