Sensor failure detection and diagnosis via polynomial chaos theory - Part I: Theoretical background

Weilin Li, Xiaobin Zhang, Wenli Yao, Huimin Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Sensors are widely used in engineering applications to provide measurements to the control and protection centers. In order to achieve higher level reliability of the whole system in engineering applications, polynomial chaos theory (PCT) is applied for sensor failure detection and diagnosis (SFDD) considering parameter uncertainties. The proposed approach allows for independent model development by propagation of uncertainty through systems. First, a review of the SFDD methods both at home and abroad is given. Then, the standard process for PCT expansion, together with the proposed new SFDD algorithm is presented. Simulation verification with a DC/DC boost converter has also been done, and the results show good consistency with the theoretical analysis.

Original languageEnglish
Title of host publicationProceedings of 2013 Chinese Intelligent Automation Conference
Subtitle of host publicationIntelligent Automation
Pages97-105
Number of pages9
DOIs
StatePublished - 2013
Event2013 Chinese Intelligent Automation Conference, CIAC 2013 - Yangzhou, Jiangsu, China
Duration: 23 Aug 201325 Aug 2013

Publication series

NameLecture Notes in Electrical Engineering
Volume254 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference2013 Chinese Intelligent Automation Conference, CIAC 2013
Country/TerritoryChina
CityYangzhou, Jiangsu
Period23/08/1325/08/13

Keywords

  • Data reconstruction
  • Parameter uncertainty
  • Polynomial chaos theory
  • Sensor failure detection and diagnosis

Fingerprint

Dive into the research topics of 'Sensor failure detection and diagnosis via polynomial chaos theory - Part I: Theoretical background'. Together they form a unique fingerprint.

Cite this