化工学报2019，Vol.70Issue(9)：3441-3448,8.DOI:10.11949/0438-1157.20190349
基于变量分组DTW-MCVA的不等长间歇过程故障检测方法
Fault detection method of unequal-length batch process based on VGDTW-MCVA
摘要
Abstract
Aiming at the problem that batch data synchronization in unequal-length batch process monitoring fails to fully exploit local information, this paper proposes a variable grouping dynamic time warping-multiway canonical variate analysis (VGDTW-CVA) algorithm for unequal-length batch process fault detection. First, the mutual information matrix is used to describe the correlation between variables of unequal-length batch process, and all the variables are divided into serval groups based on mutual information matrix. Then use the DTW algorithm to synchronize each variable group separately, and integrate the synchronized variable groups into a complete 3D data set. Finally, MCVA method is utilized to establish dynamic monitoring model for online monitoring of batch production process. The simulation results on the penicillin simulation system show that the VGDTW-MCVA has better monitoring effect for the unequal-length batch production process.关键词
不等长间歇过程/局部信息挖掘/变量分组/互信息/动态时间规整/多向典型变量分析/故障检测Key words
unequal-length batch process/local information exploiting/variable grouping/mutual information/dynamic time warping/canonical variate analysis/fault detection分类
信息技术与安全科学引用本文复制引用
于蕾,邓晓刚,曹玉苹,路凯琪..基于变量分组DTW-MCVA的不等长间歇过程故障检测方法[J].化工学报,2019,70(9):3441-3448,8.基金项目
山东省重点研发计划项目(2018GGX101025) （2018GGX101025）
中央高校基本科研业务费专项资金(17CX02054) （17CX02054）
国家自然科学基金项目(61403418,21606256) （61403418,21606256）
山东省自然科学基金项目(ZR2014FL016,ZR2016FQ21,ZR2016BQ14) （ZR2014FL016,ZR2016FQ21,ZR2016BQ14）
山东省高等学校科技计划项目(J18KA359) （J18KA359）
