Nonlinear Fault Separation for Redundancy Process Variables Based on FNN in MKFDA Subspace

Ying-Ying Su,Xiao-Gang Deng,Tai-Fu Li,Cheng Zeng,Jing-Zhe Li,Shan Liang

doi:10.1155/2014/729763

Abstract

Nonlinear faults are difficultly separated for amounts of redundancy process variables in process industry. This paper introduces an improved kernel fisher distinguish analysis method (KFDA). All the original process variables with faults are firstly optimally classified in multi-KFDA (MKFDA) subspace to obtain fisher criterion values. Multikernel is used to consider different distributions for variables. Then each variable is eliminated once from original sets, and new projection is computed with the same MKFDA direction. From this, differences between new Fisher criterion values and the original ones are tested. If it changed obviously, the effect of eliminated variable should be much important on faults called false nearest neighbors (FNN). The same test is applied to the remaining variables in turn. Two nonlinear faults crossed in Tennessee Eastman process are separated with lower observation variables for further study. Results show that the method in the paper can eliminate redundant and irrelevant nonlinear process variables as well as enhancing the accuracy of classification.

Highlights

With developments of modern process industry, multivariate monitor from sensors has showed their multicollinearity, nonlinear correlative coupling, time delay, and redundancy
This paper introduces an improved kernel fisher distinguish analysis method (KFDA)
false nearest neighbors (FNN) in MKFDA subspace is studied in the paper

Summary

Introduction

With developments of modern process industry, multivariate monitor from sensors has showed their multicollinearity, nonlinear correlative coupling, time delay, and redundancy. Right ratio of fault classification decreases with multivariate and redundancy process variables. Many attentions have been paid on two points of view that are variable selection and dimension reduction [3, 4]. Among the study of variable selection, the existed methods can be broadly classified into three categories: random search techniques, measure-based method, and intelligent computation. Each process variable is directly deleted or involved in the classification model one time in turn to search the most suitable input sets under a certain criterion, such as forward selection, backward selection, and stepwise that are simple and realized methods [5]

Objectives

Results

Conclusion