Multimode Process Monitoring Based on Fuzzy C-Means in Locality Preserving Projection Subspace
(0) For complex industrial processes with multiple operating conditions, it is important to develop effective monitoring algorithms to ensure the safety of the producing processes. This paper proposes a novel monitoring strategy based on fuzzy c-means (FCM). First, the high dimensional historical data are transferred to a low dimensional subspace space by locality preserving projection (LPP). Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operating mode. After that, the distance statistics of each cluster are integrated though the membership values into a novel BID monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman (TE) benchmark process.
Multimode process monitoring fuzzy c-means locality preserving projection integrated monitoring index Tennessee Eastman process.
Xiang Xie Hongbo Shi
Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of
国内会议
厦门
英文
1-6
2012-08-01（万方平台首次上网日期，不代表论文的发表时间）