Modified non-Gaussian multivariate statistical process monitoring based on the Gaussian distribution transformation

作     者：Du, Wenyou Zhang, Yingwei Zhou, Wei 

作者机构：Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China 

出 版 物：《JOURNAL OF PROCESS CONTROL》 (工艺过程控制杂志)

年 卷 期：2020年第85卷第0期

页      面：1-14页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 0817[工学-化学工程与技术] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 071101[理学-系统理论] 

主　　题：Multivariate statistical process monitoring Independent component analysis Gaussian distribution transformation Electrical fused magnesia furnace 

摘      要：Independent component analysis (ICA) has been applied for non-Gaussian multivariate statistical process monitoring (MSPM) for several years. As the independent components do not satisfy the multivariate Gaussian distribution, a missed alarm occurs when monitoring with traditional statistics. In this paper, we propose a Gaussian distribution transformation (GDT)-based monitoring method. Independent components are first transformed into approximate Gaussian distributions through the proposed nonlinear mapping. Then, we propose new statistics and their control limits to reduce missed alarms. The proposed method is particularly suitable for slight magnitude fault and early-stage fault detection. The ratio part of the area above the curve (RPAAC) is developed to evaluate the performance in fault detection. The experimental results from a synthetic example show the effectiveness of our proposed method. We also apply our method to monitor an electrical fused magnesia furnace (EFMF), and eruption and furnace wall melt faults can be detected in time. (C) 2017 Published by Elsevier Ltd.