Multimode Process Monitoring Based on Sparse Principal Component Selection and Bayesian Inference-Based Probability

Xiaodong Jiang,Haitao Zhao,Bo Jin

doi:10.1155/2015/465372

Abstract

According to the demand for diversified products, modern industrial processes typically have multiple operating modes. At the same time, variables within the same mode often follow a mixture of Gaussian distributions. In this paper, a novel algorithm based on sparse principal component selection (SPCS) and Bayesian inference-based probability (BIP) is proposed for multimode process monitoring. SPCS can be formulated as a just-in-time regression between all PCs and each sample. SPCS selects PCs according to the nonzero regression coefficients which indicate the compact expression of the sample. This expression is necessarilydiscriminative: amongst all subset of PCs, SPCS selects the PCs which most compactly express the sample and rejects all other possible but less compact expressions. BIP is utilized to compute the posterior probabilities of each monitored sample belonging to the multiple components and derive an integrated global probabilistic index for fault detection of multimode processes. Finally, to verify its superiority, the SPCS-BIP algorithm is applied to the Tennessee Eastman (TE) benchmark process and a continuous stirred-tank reactor (CSTR) process.

Highlights

Over the past two decades, with the development of complex chemical processes and the growing demand of plant safety and stable product quality, timely process monitoring is gaining importance
Bayesian inference probability (BIP) is utilized to compute the posterior probabilities of each monitored sample belonging to the multiple components and derive an integrated global probabilistic index for fault detection of multimode processes
Given that the modern industrial processes typically have multiple operating modes, BIP is utilized to compute the posterior probabilities of each monitored sample belonging to the multiple components and derive an integrated global probabilistic index for fault detection of multimode processes

Summary

Introduction

Over the past two decades, with the development of complex chemical processes and the growing demand of plant safety and stable product quality, timely process monitoring is gaining importance. Because large amounts of data can be gathered by the use of distributed control systems (DCSs), multivariate statistical process monitoring (MSPM) algorithms have received great attention Among these algorithms, principal component analysis (PCA) and partial least squares (PLS) are the most widely used algorithms [1,2,3,4,5,6,7,8]. Zhao et al [24] presented a multiple principal component analysis (MPCA) algorithm that selects one suitable model to monitor multimode processes. There are still some issues that need to be resolved; the most important one is how to select the key principal components (PCs) when using one suitable model for process monitoring. Togkalidou et al [32] noted that the PCs with larger variance do not always contain much information for prediction This issue is insufficiently discussed in PCA-based process monitoring, and the standard PC selection is still not established. In order to verify the superiority of the SPCS-BIP algorithm, it is applied to the Tennessee Eastman (TE) benchmark problem and a continuous stirred-tank reactor (CSTR) process

Preliminaries

Case Studies on the TE and CSTR Process

Conclusions