文献类型：期刊文献

英文题名：Multimode Process Monitoring and Fault Detection: A Sparse Modeling and Dictionary Learning Method

作者：Peng, Xin[1];Tang, Yang[1];Du, Wenli[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2017

卷号：64

期号：6

起止页码：4866

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

收录：;EI(收录号：20173604130425);WOS:【SCI-EXPANDED(收录号:WOS:000401328500054)】；

基金：This work was supported in part by the National Science Foundation of China under Grant 61590923, Grant 61422303, and Grant 21376077 and in part by the "Shu Guang" Project supported by the Shanghai Municipal Education Commission and the Shanghai Education Development Foundation.

语种：英文

外文关键词：Feature extraction; locality preserving projection (LPP); non-Gaussian process; performance monitoring; sparse coding

摘要：This study focuses on the performance monitoring of a non-Gaussian process with multiple operation conditions. By utilizing the Bayesian inference technique, the proposed method, locality preserving sparse modeling, can automatically identify the current operation condition. Then, the feature of the data structure is extracted by locality preserving projections (LPP) and modeled by the sparse modeling technique. This hybrid framework of sparse modeling and LPP provides a robust and accurate paradigm for process data clustering and monitoring. The validity and effectiveness of this approach are verified by applying it to both a synthetic numerical example and the Tennessee Eastman process benchmark process.