Machine learning approach to impact load estimation using fiber Bragg grating sensors

Clyde K. Coelho, Cristobal Hiche, Aditi Chattopadhyay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Automated detection of damage due to impact in composite structures is very important for aerospace structural health monitoring (SHM) applications. Fiber Bragg grating (FBG) sensors show promise in aerospace applications since they are immune to electromagnetic interference and can support multiple sensors in a single fiber. However, since they only measure strain along the length of the fiber, a prediction scheme that can estimate loading using randomly oriented sensors is key to damage state awareness. This paper focuses on the prediction of impact loading in composite structures as a function of time using a support vector regression (SVR) approach. A time delay embedding feature extraction scheme is used since it can characterize the dynamics of the impact using the sensor signal from the FBGs. The efficiency of this approach has been demonstrated on simulated composite plates and wing structures. Training with impacts at four locations with three different energies, the constructed framework is able to predict the force-time history at an unknown impact location to within 12 percent on the composite plate and to within 10 percent on a composite wing when the impact was within the sensor network region.

Original languageEnglish (US)
Title of host publicationSmart Sensor Phenomena, Technology, Networks, and Systems 2010
DOIs
StatePublished - Jun 18 2010
EventSmart Sensor Phenomena, Technology, Networks, and Systems 2010 - San Diego, CA, United States
Duration: Mar 8 2010Mar 10 2010

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7648
ISSN (Print)0277-786X

Other

OtherSmart Sensor Phenomena, Technology, Networks, and Systems 2010
CountryUnited States
CitySan Diego, CA
Period3/8/103/10/10

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Keywords

  • Carbon fiber composite
  • Damage estimation
  • Fiber Bragg grating sensors
  • Impact
  • Structural health monitoring
  • Support vector regression
  • Time delay embedding
  • Wing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Coelho, C. K., Hiche, C., & Chattopadhyay, A. (2010). Machine learning approach to impact load estimation using fiber Bragg grating sensors. In Smart Sensor Phenomena, Technology, Networks, and Systems 2010 [764810] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 7648). https://doi.org/10.1117/12.847884