CAJ | 학술논문

实际化工过程监控数据具有非线性特征且易受随机噪声影响。将核主元分析(KPCA)方法与最大方差展开(MVU)特征提取算法相结合，提出一种基于 KPCA-MVU 的噪声环境下非线性过程故障检测新方法。改进算法在对非线性噪声数据的降维过程中，首先对各样本点的邻域范围采用局部KPCA方法识别并剔除过程数据的噪声，再提取输入数据空间中的非线性主元；其次，在保持近邻点间欧式距离不变的前提下， MVU通过旋转平移等变换在低维特征空间中展开高维数据流形的同时保持数据的全局几何结构。噪声环境下 TE 过程的仿真分析和丙烯腈聚合过程的实验研究结果表明，基于改进方法构建的过程故障检测模型可有效改善基本MVU和KPCA方法对非线性噪声数据的特征提取性能，有效增强了对噪声的鲁棒性。
실제화공과정감공수거구유비선성특정차역수수궤조성영향。장핵주원분석(KPCA)방법여최대방차전개(MVU)특정제취산법상결합，제출일충기우 KPCA-MVU 적조성배경하비선성과정고장검측신방법。개진산법재대비선성조성수거적강유과정중，수선대각양본점적린역범위채용국부KPCA방법식별병척제과정수거적조성，재제취수입수거공간중적비선성주원；기차，재보지근린점간구식거리불변적전제하， MVU통과선전평이등변환재저유특정공간중전개고유수거류형적동시보지수거적전국궤하결구。조성배경하 TE 과정적방진분석화병희정취합과정적실험연구결과표명，기우개진방법구건적과정고장검측모형가유효개선기본MVU화KPCA방법대비선성조성수거적특정제취성능，유효증강료대조성적로봉성。
Actual chemical process monitoring data have strong nonlinear behavior and are easily disturbed by random noises. A novel kernel principle component analysis (KPCA) - maximum variance unfolding (MVU) based fault detection method for nonlinear process under noise environment is proposed by combining KPCA and MVU feature extraction algorithms. In the dimension reduction process of nonlinear noisy data, local KPCA method is applied to identify and eliminate the noise in the process data in the neighborhood of sample points; and then the nonlinear principal components are extracted in the input data space. Next, under the condition of keeping the Euclidean distances between neighbor points unchanged, MVU is used to map the original high dimension data space to a low dimension embedding space while preserving the data global geometric structure via coordinate rotation and translation transformation. Simulation results of TE process under noise environment and experiment results of acrylonitrile polymerization process show that the improved KPCA-MVU based fault detection model can improve the feature extraction performance of standard KPCA and MVU algorithms for nonlinear noisy data, and effectively enhance the robustness against noise.