Classification of abnormal plant operation using multiple process variable trends

被引：40

作者：

Wong, JC ^{[1
]}

McDonald, KA ^{[1
]}

Palazoglu, A ^{[1
]}

机构：

[1] Univ Calif Davis, Dept Chem Engn & Mat Sci, Davis, CA 95616 USA

来源：

JOURNAL OF PROCESS CONTROL | 2001年 / 11卷 / 04期

关键词：

process diagnosis; hidden Markov models; back-propagation neural network;

D O I：

10.1016/S0959-1524(00)00011-1

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper illustrates two strategies for the detection and classification of abnormal process operating conditions in which multiple process variable trends are available. The first strategy uses a hidden Markov model (HMM) for overall process classification while the second method uses a back-propagation neural network (BPNN) to determine the overall process classification. The methods are compared in terms of their ability to detect and correctly diagnose a variety of abnormal operating conditions for a non-isothermal CSTR simulation. For the case study problem, the BPNN method resulted in better classification accuracy with a moderate increase in training time compared with the HMM approach. (C) 2001 Elsevier Science Ltd. All rights reserved.

引用

页码：409 / 418

页数：10