DETECTION OF GROSS ERRORS IN DATA RECONCILIATION BY PRINCIPAL COMPONENT ANALYSIS

被引：118

作者：

TONG, HW ^{[1
]}

CROWE, CM ^{[1
]}

机构：

[1] MCMASTER UNIV, DEPT CHEM ENGN, HAMILTON, ON L8S 4L7, CANADA

来源：

AICHE JOURNAL | 1995年 / 41卷 / 07期

关键词：

D O I：

10.1002/aic.690410711

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Statistical testing provides a tool for engineers and operators to judge the validity of process measurements and data reconciliation. Univariate, maximum power and chi-square tests have been widely used for this purpose. Their performance, however has not always been satisfactory. A new class of test statistics for detection and identification of gross errors is presented based on principal component analysis and is compared to the other statistics. It is shown that the new test is capable of detecting gross errors of small magnitudes and has substantial power to correctly identify the variables in error, when the other tests fail.

引用

页码：1712 / 1722

页数：11

共 50 条

[31] Principal component analysis clarifies the component structure of ERPs following errors
Arbel, Yael
Donchin, Emanuel
[J]. PSYCHOPHYSIOLOGY, 2006, 43 : S20 - S21
[32] On the performance of principal component analysis in multiple gross error identification
Jiang, QY
Sánchez, M
Bagajewicz, M
[J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1999, 38 (05) : 2005 - 2012
[33] Application of Principal Component Analysis for Outlier Detection in Heterogeneous Traffic Data
Saha, Pritam
Roy, Nabanita
Mukherjee, Deotima
Sarkar, Ashoke Kumar
[J]. 7TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT 2016) / THE 6TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2016) / AFFILIATED WORKSHOPS, 2016, 83 : 107 - 114
[34] Principal component analysis of hyperspectral data for early detection of mould in cheeselets
Farrugia, Jessica
Griffin, Sholeem
Valdramidis, Vasilis P.
Camilleri, Kenneth
Falzon, Owen
[J]. CURRENT RESEARCH IN FOOD SCIENCE, 2021, 4 : 18 - 27
[35] Bad Data Detection in PMU Measurements using Principal Component Analysis
Mahapatra, Kaveri
Chaudhuri, Nilanjan Ray
Kavasseri, Rajesh
[J]. 2016 NORTH AMERICAN POWER SYMPOSIUM (NAPS), 2016,
[36] Comparison of Principal Component Analysis Techniques for PMU Data Event Detection
Souto, L.
Melendez, J.
Herraiz, S.
[J]. 2020 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2020,
[37] A brief review of recent in data reconciliation and gross error detection/estimation
Bagajewicz, MJ
[J]. LATIN AMERICAN APPLIED RESEARCH, 2000, 30 (04) : 335 - 342
[38] Correntropy based data reconciliation and gross error detection for bilinear systems
Zhang, Zhengjiang
Chan, Lester Lik Teck
Chen, Junghui
Shao, Zhijiang
[J]. CHEMICAL ENGINEERING SCIENCE, 2020, 212 (212)
[39] DATA RECONCILIATION AND GROSS ERROR-DETECTION IN CHEMICAL PROCESS NETWORKS
TAMHANE, AC
MAH, RSH
[J]. TECHNOMETRICS, 1985, 27 (04) : 409 - 422
[40] Nonlinear Model Identification and Data Reconciliation Using Kernel Principal Component Regression
Marimuthu, Karthikeyan
Narasimhan, Shankar
[J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2019, 58 (26) : 11224 - 11233

← 1 2 3 4 5 →