非线性PCA方法在间歇过程性能监视和故障诊断中的应用

针对间歇生产过程的特点，基于多方向主元分析方法（ＭＰＣＡ）和非线性理论，提出具有实时性的非线性最小窗口ＰＣＡ方法，分析复杂非线性间歇过程的性能，诊断异常事件的原因，讨论最小窗口ＰＣＡ方法的建模方法，过程性能监视和故障诊断基本原理，仿真实例验证该方法的有效性，最小窗口ＰＣＡ方法突破ＭＰＣＡＹ一性化的建模方式，创新性地构造了适合间歇生产过程特点的多模型结构非线性建模方法，并侧重于在线间歇过程性能监视和

A Nonlinear PCA Method and its Applications in the Batch Process Performance Monitoring and Fault Diagnosis

In view of characteristics of batch processes, this paper proposes a new real time and nonlinear minimum window principal component analysis (MWPCA) method based on multiway principal component analysis and nonlinear theory. In this paper, the complex nonlinear performances of batch processes are analyzed and the causes of abnormal events are diagnosed. MWPCA method breaks through linear MPCA modeling with single model structure and innovates in a nonlinear multi\|model structure for batch process modeling. The method emphasizes particularly on real-time characteristic in on\|line batch process performance monitoring and eliminates error caused by predicting future measurements of process variables, increases the accuracy of process performance monitoring and fault diagnosis. MWPCA modeling procedures and the principle of batch process performance monitoring and fault diagnosis are discussed in detail. Simulations verify the validity of MWPCA method.