Use of Principal Component Analysis for Sensor Fault Identification

Ricardo DUNIA; S. Joe QIN; Thomas F. EDGAR; Thomas J. MCAVOY

doi:10.1016/0098-1354(96)00128-7

Use of Principal Component Analysis for Sensor Fault Identification

Ricardo DUNIA^*, S. Joe QIN, Thomas F. EDGAR, Thomas J. MCAVOY

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

69 Citations (Scopus)

Abstract

This paper make use of PCA for sensor fault identification via reconstruction. The principal component model captures the measurement correlations and reconstructs each variable to define associated residuals and a Sensor Validity Index (SVI). The filter applied to the SVI adds an important feature for sensor fault isolation because reduces the effect of false alarms and allows the identification of different types of sensor faults.

Original language	English
Pages (from-to)	S713-S718
Number of pages	6
Journal	Computers and Chemical Engineering
Volume	20
Issue number	Suppl. 1
DOIs	https://doi.org/10.1016/0098-1354(96)00128-7
Publication status	Published - 1996
Externally published	Yes

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10.1016/0098-1354(96)00128-7

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@article{966590109ec3468a998be605a7562cc9,

title = "Use of Principal Component Analysis for Sensor Fault Identification",

abstract = "This paper make use of PCA for sensor fault identification via reconstruction. The principal component model captures the measurement correlations and reconstructs each variable to define associated residuals and a Sensor Validity Index (SVI). The filter applied to the SVI adds an important feature for sensor fault isolation because reduces the effect of false alarms and allows the identification of different types of sensor faults.",

author = "Ricardo DUNIA and QIN, {S. Joe} and EDGAR, {Thomas F.} and MCAVOY, {Thomas J.}",

year = "1996",

doi = "10.1016/0098-1354(96)00128-7",

language = "English",

volume = "20",

pages = "S713--S718",

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T1 - Use of Principal Component Analysis for Sensor Fault Identification

AU - DUNIA, Ricardo

AU - QIN, S. Joe

AU - EDGAR, Thomas F.

AU - MCAVOY, Thomas J.

PY - 1996

Y1 - 1996

N2 - This paper make use of PCA for sensor fault identification via reconstruction. The principal component model captures the measurement correlations and reconstructs each variable to define associated residuals and a Sensor Validity Index (SVI). The filter applied to the SVI adds an important feature for sensor fault isolation because reduces the effect of false alarms and allows the identification of different types of sensor faults.

AB - This paper make use of PCA for sensor fault identification via reconstruction. The principal component model captures the measurement correlations and reconstructs each variable to define associated residuals and a Sensor Validity Index (SVI). The filter applied to the SVI adds an important feature for sensor fault isolation because reduces the effect of false alarms and allows the identification of different types of sensor faults.

UR - http://www.scopus.com/inward/record.url?scp=0029723020&partnerID=8YFLogxK

U2 - 10.1016/0098-1354(96)00128-7

DO - 10.1016/0098-1354(96)00128-7

M3 - Journal Article (refereed)

SN - 0098-1354

VL - 20

SP - S713-S718

JO - Computers and Chemical Engineering

JF - Computers and Chemical Engineering

IS - Suppl. 1

ER -

Use of Principal Component Analysis for Sensor Fault Identification

Abstract

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