Statistics Analysis of PCA-based Sensor Fault Detection

被引：2
|
作者：
Qiu, Tian [1 ]
Zhu, Xiang [1 ]
机构：
[1] N China Elect Power Univ, Key Lab Measurement & Control Ind Proc, Beijing 102206, Peoples R China
来源：
FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE II, PTS 1-6 | 2012年 / 121-126卷
关键词：
Principal Component Analysis; Fault detection; Sensor; PRINCIPAL COMPONENT ANALYSIS;
D O I：
10.4028/www.scientific.net/AMM.121-126.1085
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
It is of importance to analyze relations between Hawkins' T-H(2) statistic and process variables mean and variance. The formula of T-H(2) statistic mean is derived, and its variation law with process variable mean and variance is investigated. Simulation data indicates that the variation law of T-H(2) is similar to SPE. Changing of process variable mean and variance can both enlarge T-H(2), while process noise can make the statistic oscillating.
引用
收藏
页码：1085 / 1089
页数：5
相关论文
共 50 条
  • [11] Fault detection using multiscale PCA-based moving window GLRT
    Sheriff, M. Ziyan
    Mansouri, Majdi
    Karim, M. Nazmul
    Nounou, Hazem
    Nounou, Mohamed
    [J]. JOURNAL OF PROCESS CONTROL, 2017, 54 : 47 - 64
  • [12] Statistical fault detection using PCA-based GLR hypothesis testing
    Harrou, Fouzi
    Nounou, Mohamed N.
    Nounou, Hazem N.
    Madakyaru, Muddu
    [J]. JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2013, 26 (01) : 129 - 139
  • [13] An enhanced PCA-based chiller sensor fault detection method using ensemble empirical mode decomposition based denoising
    Li, Guannan
    Hu, Yunpeng
    [J]. ENERGY AND BUILDINGS, 2019, 183 : 311 - 324
  • [14] An Improved Distributed PCA-Based Outlier Detection in Wireless Sensor Network
    Zheng, Wentian
    Yang, Lijun
    Wu, Meng
    [J]. CLOUD COMPUTING AND SECURITY, PT V, 2018, 11067 : 37 - 49
  • [15] Generalization and analysis of sufficient conditions for PCA-based fault detectability and isolability
    Mnassri, Baligh
    El Adel, El Mostafa
    Ouladsine, Mustapha
    [J]. ANNUAL REVIEWS IN CONTROL, 2013, 37 (01) : 154 - 162
  • [16] PCA-based method of soft fault detection and identification for the ongoing commissioning of chillers
    Cotrufo, Nunzio
    Zmeureanu, Radu
    [J]. ENERGY AND BUILDINGS, 2016, 130 : 443 - 452
  • [17] A reformative PCA-based fault detection method suitable for power plant process
    Niu, Z
    Liu, JZ
    Niu, YG
    Pan, YS
    [J]. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9, 2005, : 2133 - 2138
  • [18] Enhanced Monitoring using PCA-based GLR Fault Detection and Multiscale Filtering
    Harrou, Fouzi
    Nounou, Mohamed N.
    Nounou, Hazem N.
    [J]. PROCEEDINGS OF THE 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN CONTROL AND AUTOMATION (CICA), 2013, : 1 - 8
  • [19] Classification of gene expression data using PCA-based fault detection and identification
    Josserand, Timothy M.
    [J]. 2008 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS, 2008, : 72 - 75
  • [20] A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method
    Hu, Yunpeng
    Chen, Huanxin
    Li, Guannan
    Li, Haorong
    Xu, Rongji
    Li, Jiong
    [J]. ENERGY AND BUILDINGS, 2016, 112 : 270 - 278
12345