Fault detection and diagnosis based on improved PCA with JAA method in VAV systems

被引：85
|
作者：
Du, Zhimin [1 ]
Jin, Xinqiao [1 ]
Wu, Lizhou [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Dept Mech Engn, Shanghai 200030, Peoples R China
来源：
BUILDING AND ENVIRONMENT | 2007年 / 42卷 / 09期
关键词：
VAV systems; fault characteristic; principal component analysis; joint angle analysis; sensor; fixed and drifting biases;
D O I：
10.1016/j.buildenv.2006.08.011
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
In this paper, improved principal component analysis (PCA) with joint angle analysis (JAA) is presented to detect and diagnose both fixed and drifting biases of sensors in variable air volume (VAV) systems. Fault characteristic concerned in PID controller in the VAV systems is analyzed and discussed. The squared prediction error (SPE) plot based on PCA is used to detect the sensor fixed and drifting biases. Then the JAA plot instead of conventional contribution plot is used to diagnose the faults. And they are tested and evaluated online in a simulated centralized VAV air-conditioning systems. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码：3221 / 3232
页数：12
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