基于双层自适应集成残差主成分分析的复杂非线性过程监测

Two-Layer Adaptive Ensemble Residual Principal Component Analysis for Complex Nonlinear Process Monitoring

多元统计监测方法常使用正常数据选取特征,而现实过程中,不同的故障将影响不同的特征,并且这些特征可能随着时间和控制系统的作用而变化。当故障发生并随时间变化时,要想获得更好的故障检测能力,就需要聚集有效的故障敏感特征。本文提出了一种双层自适应集成残差主成分分析(AERPCA)模型,其子模型包含不同的特征,并突出地呈现一个或多个相关故障。首先,根据正常数据计算主成分分析(PCA)特征,利用不同特征构建线性子模型和相应的残差空间。考虑到残差空间的非线性特性及有效特征更为分散,采用核PCA(KPCA)提取不同的特征并组成同一残差空间下不同KPCA子模型。然后,利用贝叶斯方法获取集成KPCA子模型,完成各残差空间的划分和集成。最后,在主空间中获得多个线性子模型以及在残差空间中获得多个集成的非线性子模型后,利用滑动窗口确定当前时刻监控效果最好的模型。采用田纳西-伊士曼过程验证了AERPCA的有效性。

Multivariate statistical monitoring method uses normal operation data to select features.However,in practical processes,different faults will affect different features,and these features may change over time and the action of the control system.When faults occur and change over time,in order to achieve better fault detection abilities,it is necessary to gather effective fault-sensitive features.This paper presents a two-layer adaptive ensemble residual principal component analysis model(AERPCA),whose sub-models consist of different features and prominently present one or several related faults.First,the principal component analysis(PCA)features are calculated according to normal data,and different features are used to construct the linear sub-models and the corresponding residual spaces.Considering that the nonlinear and effective features of the residual space are more dispersed,kernel PCA(KPCA)is used for extracting and forming different KPCA sub-models in the same area by the second feature selection.Then,Bayesian method is used to form an integrated KPCA sub-model and complete the division and integration of each residual space.Finally,after obtaining multiple PCA sub-models in the main space and the integrated KPCA sub-models in the residual space,the sliding window is used to determine the model with the best monitoring effect at the current time.Tennessee Eastman process is used to verify the effectiveness of AERPCA.