Skip to main navigation Skip to search Skip to main content

Noise reduction of FBG sensor signal by using a Wavelet Transform

  • Yo Han Cho*
  • , Minho Song
  • *Corresponding author for this work
  • Jeonbuk National University

Research output: Contribution to conferenceConference paperpeer-review

Abstract

We constructed a FBG (fiber Bragg grating) sensor system based on a fiber-optic Sagnac interferometer. A fiber-optic laser source is used as a strong light source to attain high signal-to-noise ratio. However the unstable output power and coherence noises of the fiber laser made it hard to separate the FBG signals from the interference signals of the fiber coils. To reduce noises and extract FBG sensor signals, we used a Gaussian curve-fitting and a wavelet transform. The wavelet transform is a useful tool for analyzing and denoising output signals. The feasibility of the wavelet transform denoising process is presented with the preliminary experimental results, which showed much better accuracy than the case with only the Gaussian curve-fitting algorithm.

Original languageEnglish
Title of host publicationOptical Sensors 2011; and Photonic Crystal Fibers V
DOIs
StatePublished - 2011
EventOptical Sensors 2011; and Photonic Crystal Fibers V - Prague, Czech Republic
Duration: 2011.04.182011.04.20

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8073
ISSN (Print)0277-786X

Conference

ConferenceOptical Sensors 2011; and Photonic Crystal Fibers V
Country/TerritoryCzech Republic
CityPrague
Period11.04.1811.04.20

Keywords

  • FBG
  • Gaussian curve-fitting
  • Sagnac interferometer
  • Temperature sensor
  • Wavelet transform

Quacquarelli Symonds(QS) Subject Topics

  • Materials Science
  • Computer Science & Information Systems
  • Mathematics
  • Engineering - Electrical & Electronic
  • Engineering - Petroleum
  • Data Science
  • Physics & Astronomy

Fingerprint

Dive into the research topics of 'Noise reduction of FBG sensor signal by using a Wavelet Transform'. Together they form a unique fingerprint.

Cite this