独立成分分析
故障检测与隔离
过程(计算)
计算机科学
水准点(测量)
主成分分析
盲信号分离
鉴定(生物学)
断层(地质)
模式识别(心理学)
组分(热力学)
数据挖掘
特征提取
代表(政治)
人工智能
执行机构
地震学
法学
地理
物理
地质学
频道(广播)
大地测量学
操作系统
热力学
政治
生物
植物
计算机网络
政治学
作者
Jongmin Lee,ChangKyoo Yoo,In‐Beum Lee
标识
DOI:10.1016/j.jprocont.2003.09.004
摘要
In this paper we propose a new statistical method for process monitoring that uses independent component analysis (ICA). ICA is a recently developed method in which the goal is to decompose observed data into linear combinations of statistically independent components [1], [2]. Such a representation has been shown to capture the essential structure of the data in many applications, including signal separation and feature extraction. The basic idea of our approach is to use ICA to extract the essential independent components that drive a process and to combine them with process monitoring techniques. I2, Ie2 and SPE charts are proposed as on-line monitoring charts and contribution plots of these statistical quantities are also considered for fault identification. The proposed monitoring method was applied to fault detection and identification in both a simple multivariate process and the simulation benchmark of the biological wastewater treatment process, which is characterized by a variety of fault sources with non-Gaussian characteristics. The simulation results clearly show the power and advantages of ICA monitoring in comparison to PCA monitoring.
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