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
相关论文
共 50 条
  • [1] Fault detection and diagnosis based on improved PCA with JAA method in VAV systems
    Department of Mechanical Engineering, Shanghai Jiao Tong University, China
    Build. Environ., 1600, 9 (3221-3232):
  • [2] PCA-FDA-based fault diagnosis for sensors in VAV systems
    Du, Zhimin
    Jin, Xinqiao
    Wu, Lizhou
    HVAC&R RESEARCH, 2007, 13 (02): : 349 - 367
  • [3] An improved PCA fault detection for the diagnosis
    Pessel, N.
    Balmat, J-F.
    Lafont, F.
    Bonnal, J.
    PROCEEDINGS OF THE 9TH WSEAS INTERNATIONAL CONFERENCE ON AUTOMATIC CONTROL, MODELING & SIMULATION, 2007, : 303 - +
  • [4] The impact of improved PCA method based on anomaly detection on chiller sensor fault detection
    Liang, Aosong
    Hu, Yunpeng
    Li, Guannan
    INTERNATIONAL JOURNAL OF REFRIGERATION, 2023, 155 : 184 - 194
  • [5] A fault detection and diagnosis strategy of VAV air-conditioning systems for improved energy and control performances
    Qin, HY
    Wang, SW
    ENERGY AND BUILDINGS, 2005, 37 (10) : 1035 - 1048
  • [6] A fault detection method based on improved local entropy PCA for industrial processes
    Guo J.-Y.
    Liu Y.-C.
    Li Y.
    Gao Xiao Hua Xue Gong Cheng Xue Bao/Journal of Chemical Engineering of Chinese Universities, 2019, 33 (04): : 922 - 932
  • [7] Improved Kernel PCA Based on Wavelet for Fault Detection
    Wu, Hongyan
    Huang, Daoping
    2008 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL 2, PROCEEDINGS, 2008, : 8 - 12
  • [8] An Improved PCA-based Fault Detection Method for non-Gaussian Systems Using SIP Criterion
    Ren, Mi F.
    Liang, Yan
    Gong, Ming Y.
    2019 IEEE 15TH INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA), 2019, : 905 - 910
  • [9] A Method of Fault Diagnosis Based on PCA and Bayes Classification
    Shi, Xiangrong
    Liang, Jun
    Ye, Lubin
    Hu, Bin
    2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2010, : 5628 - 5631
  • [10] Fault Diagnosis Method Based on Moving Window PCA
    Lu, Binzhang
    Zhao, Yuhong
    Mao, Zhenhua
    CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 185 - 188
12345