王普,李春蕾,高学金,常鹏,齐咏生.基于角结构统计量的MKECA间歇过程故障监测[J].仪器仪表学报,2017,38(1):174-180
基于角结构统计量的MKECA间歇过程故障监测
Research on batch process monitoring method based on multi way kernel entropy component analysis and angular structure statistic
  
DOI:
中文关键词:  核熵成分分析  角结构统计量  核密度估计  故障监测
英文关键词:kernel entropy component analysis (KZCA)  angular structure statistics  kernel density estimation  fault detection
基金项目:国家自然科学基金(61174109,61364009,61640312)、北京市自然科学基金(4172007)项目资助
作者单位
王普 1. 北京工业大学信息学部北京100124;2. 数字社区教育部工程研究中心北京100124;3. 城市轨道交通北京实验室北京100124;4. 计算智能与智能系统北京市重点实验室北京100124 
李春蕾 1. 北京工业大学信息学部北京100124;2. 数字社区教育部工程研究中心北京100124;3. 城市轨道交通北京实验室北京100124;4. 计算智能与智能系统北京市重点实验室北京100124 
高学金 1. 北京工业大学信息学部北京100124;2. 数字社区教育部工程研究中心北京100124;3. 城市轨道交通北京实验室北京100124;4. 计算智能与智能系统北京市重点实验室北京100124 
常鹏 1. 北京工业大学信息学部北京100124;2. 数字社区教育部工程研究中心北京100124;3. 城市轨道交通北京实验室北京100124;4. 计算智能与智能系统北京市重点实验室北京100124 
齐咏生 内蒙古工业大学电力学院呼和浩特010051 
AuthorInstitution
Wang Pu 1. The Information Department of Beijing University of Technology, Beijing, 100124, China; 2. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China;3. Beijing Laboratory of Urban Rail Transit, Beijing, 100124, China;4. Beijing Laboratory of Computational Intelligence System, Beijing, 100124, China 
Li Chunlei 1. The Information Department of Beijing University of Technology, Beijing, 100124, China; 2. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China;3. Beijing Laboratory of Urban Rail Transit, Beijing, 100124, China;4. Beijing Laboratory of Computational Intelligence System, Beijing, 100124, China 
Gao Xuejin 1. The Information Department of Beijing University of Technology, Beijing, 100124, China; 2. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China;3. Beijing Laboratory of Urban Rail Transit, Beijing, 100124, China;4. Beijing Laboratory of Computational Intelligence System, Beijing, 100124, China 
Chang Peng 1. The Information Department of Beijing University of Technology, Beijing, 100124, China; 2. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China;3. Beijing Laboratory of Urban Rail Transit, Beijing, 100124, China;4. Beijing Laboratory of Computational Intelligence System, Beijing, 100124, China 
Qi Yongsheng School of Electric Power, Inner Mongolia University of Technology, Huhhot 010051,China 
摘要点击次数: 771
全文下载次数: 2499
中文摘要:
      针对间歇过程复杂非线性的特点,提出一种基于角结构统计量的多向核熵成分分析(MKECA)间歇过程监测方法。该方法首先将间歇过程数据进行标准化预处理,然后采用KECA提取间歇过程数据的主成分矩阵。研究表明,经过KECA投影后的主成分数据具有良好的角结构,因此利用主成分矩阵构造基于角结构的统计量,并且采用核密度估计算法计算其控制限。与传统的统计量相比,无需假设过程变量服从高斯分布。最后通过青霉素发酵的仿真平台和大肠杆菌实际生产过程验证,实验结果表明,相比于传统MKPCA方法,能够有效利用主成分的结构信息,明显降低了故障的误报率、漏报率。
英文摘要:
      Aiming at monitoring the batch process with complex nonlinear characteristic, a multi way kernel entropy component analysis (MKECA) method based on the angle structure statistic is proposed. In this method, the process data is firstly preprocessed, and then the principal component matrices of the batch process data are extracted by KECA. Research shows that KECA reveals angular structure relating to the Renyi entropy of the input space data set, and angular structure statistic is constructed using the principal component matrix structure. And then the control limits are calculated by the kernel density estimation algorithm. Finally, through the simulation of the penicillin fermentation and the actual production process of recombinant, the experiment results show that the proposed method effectively uses the structural information of the principal components compared to the traditional method of process monitoring. So error rate and false alarming rate are significantly lowered.
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