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

Research output: Contribution to journalReview articlepeer-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 languageEnglish
Pages (from-to)28-38
Number of pages11
JournalSensors and Actuators, A: Physical
Volume136
Issue number1
DOIs
Publication statusPublished - 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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