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

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.

关键词： Fault Detection,MICA Adaptive FCM Multi-phase Batch Process

作者: GAO Xuejin CUI Ning QI Yongsheng WANG Pu

作者单位: College of Electronic Information and Control Engineering,Beijing University of Technology,Beijing 1 College of Electric Power,Inner Mongolia University of Technology,Huhhot 010051,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 3088-3093

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）