Principal component analysis and artificial neural network based approach to analysing optical fibre sensors signals

E. Lewis; C. Sheridan; M. O'Farrell; D. King; C. Flanagan; W. B. Lyons; C. Fitzpatrick

doi:10.1016/j.sna.2007.02.012

Principal component analysis and artificial neural network based approach to analysing optical fibre sensors signals

E. Lewis, C. Sheridan, M. O'Farrell, D. King, C. Flanagan, W. B. Lyons, C. Fitzpatrick

University of Limerick

Research output: Contribution to journal › Review article › peer-review

Abstract

This paper investigates the use of artificial neural networks (ANNs) coupled with principal components analysis (PCA) to interpret complex optical spectrum and time resolved signals from optical fibre sensors. Specific reference is made to two application areas addressed by optical fibre sensors which are examples of systems deployed to measure food colour (reflection spectra) as it cooks in a full scale industrial oven and time resolved and Fourier transformed signals received from an optical time domain reflectometer (OTDR) for water monitoring. The method of analysis is different in each case but the same principles apply to each measurement.

Original language	English
Pages (from-to)	28-38
Number of pages	11
Journal	Sensors and Actuators, A: Physical
Volume	136
Issue number	1
DOIs	https://doi.org/10.1016/j.sna.2007.02.012
Publication status	Published - 1 May 2007

Keywords

Artificial neural networks processing for sensor data
Fluorescent coated optical fibres
Food quality sensor
Optical fibre colour sensor
Optical fibre temperature sensor
PCA
Water quality measurement

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10.1016/j.sna.2007.02.012

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title = "Principal component analysis and artificial neural network based approach to analysing optical fibre sensors signals",

abstract = "This paper investigates the use of artificial neural networks (ANNs) coupled with principal components analysis (PCA) to interpret complex optical spectrum and time resolved signals from optical fibre sensors. Specific reference is made to two application areas addressed by optical fibre sensors which are examples of systems deployed to measure food colour (reflection spectra) as it cooks in a full scale industrial oven and time resolved and Fourier transformed signals received from an optical time domain reflectometer (OTDR) for water monitoring. The method of analysis is different in each case but the same principles apply to each measurement.",

keywords = "Artificial neural networks processing for sensor data, Fluorescent coated optical fibres, Food quality sensor, Optical fibre colour sensor, Optical fibre temperature sensor, PCA, Water quality measurement",

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AU - Lewis, E.

AU - Sheridan, C.

AU - O'Farrell, M.

AU - King, D.

AU - Flanagan, C.

AU - Lyons, W. B.

AU - Fitzpatrick, C.

PY - 2007/5/1

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N2 - This paper investigates the use of artificial neural networks (ANNs) coupled with principal components analysis (PCA) to interpret complex optical spectrum and time resolved signals from optical fibre sensors. Specific reference is made to two application areas addressed by optical fibre sensors which are examples of systems deployed to measure food colour (reflection spectra) as it cooks in a full scale industrial oven and time resolved and Fourier transformed signals received from an optical time domain reflectometer (OTDR) for water monitoring. The method of analysis is different in each case but the same principles apply to each measurement.

AB - This paper investigates the use of artificial neural networks (ANNs) coupled with principal components analysis (PCA) to interpret complex optical spectrum and time resolved signals from optical fibre sensors. Specific reference is made to two application areas addressed by optical fibre sensors which are examples of systems deployed to measure food colour (reflection spectra) as it cooks in a full scale industrial oven and time resolved and Fourier transformed signals received from an optical time domain reflectometer (OTDR) for water monitoring. The method of analysis is different in each case but the same principles apply to each measurement.

KW - Artificial neural networks processing for sensor data

KW - Fluorescent coated optical fibres

KW - Food quality sensor

KW - Optical fibre colour sensor

KW - Optical fibre temperature sensor

KW - PCA

KW - Water quality measurement

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DO - 10.1016/j.sna.2007.02.012

M3 - Review article

AN - SCOPUS:34247241164

SN - 0924-4247

VL - 136

SP - 28

EP - 38

JO - Sensors and Actuators, A: Physical

JF - Sensors and Actuators, A: Physical

IS - 1

ER -

Principal component analysis and artificial neural network based approach to analysing optical fibre sensors signals

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