Fault Detection of Multi-phase Batch Process Based on Adaptive FCM

被引:0
作者
Gao Xuejin [1 ]
Cui Ning [1 ]
Qi Yongsheng [2 ]
Wang Pu [1 ]
机构
[1] Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
[2] Inner Mongolia Univ Technol, Coll Elect Power, Hohhot 010051, Peoples R China
来源
2014 33RD CHINESE CONTROL CONFERENCE (CCC) | 2014年
关键词
Fault Detection; MICA; Adaptive FCM; Multi-phase; Batch Process;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Batch processes have the characteristic of more operation phases in nature. The standard Fuzzy C-Means (FCM) algorithm for phase partition of batch processes needs to a given phase partition number beforehand, initialize clustering centers randomly, and is sensitive to noise and outliers. For the above problems, the adaptive FCM algorithm using clustering validity function is proposed to achieve the adaptive partition of batch process operation phases. The method obtains the initial clustering center set on the basis of maximum minimum distance rule, through adaptive iteration way determines the optimal clustering number by introducing the clustering validity function. The MICA model based on the improved phase partition method is applied to fault detection of industrial penicillin fermentation process and the experimental results verify the effectiveness of the proposed method.
引用
收藏
页码:3088 / 3093
页数:6
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