文献类型：期刊文献

英文题名：Hidden Markov model-based approach for multimode process monitoring

作者：Wang, Fan[1];Tan, Shuai[1];Shi, Hongbo[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2015

卷号：148

起止页码：51

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20240815593562);WOS:【SCI-EXPANDED(收录号:WOS:000364885900005)】；

基金：This research is supported by the National Nature Science Foundation of China (No.61374140, No.61403072) and the Fundamental Research Funds for the Central Universities (22A201514050).

语种：英文

外文关键词：Hidden Markov model; Fault detection; Multimode; Mode identification

摘要：Many industrial processes have multiple operating modes due to various factors, such as alterations of feedstock and compositions, different manufacturing strategies, fluctuations in the external environment, and various product specifications. There are just a few literatures concerning hidden Markov model in multimode process monitoring. And HMM has not been explored to deal with transitional modes. Besides, those monitoring methods fail to take advantage of internal elements of HMM. In this article, a novel monitoring scheme based on hidden Markov mode (HMM) is proposed for multimode process with transitions. To begin with, a hidden Markov model is trained on the basis of the measurement data. Then a probability ratio strategy based on HMM is developed to identify stable modes and transitional modes. Further, the Viterbi algorithm classifies samples into various modes and a new monitoring indication is built based on the elements of HMM in each mode for fault detection. At last, the effectiveness of the proposed method is demonstrated through a numerical simulation and the Tennessee Eastman process. (C) 2015 Elsevier B.V. All rights reserved.