Robust feature extraction for rapid classification of damage in composites

Clyde K. Coelho, Whitney Reynolds, Aditi Chattopadhyay

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

3 Scopus citations


The ability to detect anomalies in signals from sensors is imperative for structural health monitoring (SHM) applications. Many of the candidate algorithms for these applications either require a lot of training examples or are very computationally inefficient for large sample sizes. The damage detection framework presented in this paper uses a combination of Linear Discriminant Analysis (LDA) along with Support Vector Machines (SVM) to obtain a computationally efficient classification scheme for rapid damage state determination. LDA was used for feature extraction of damage signals from piezoelectric sensors on a composite plate and these features were used to train the SVM algorithm in parts, reducing the computational intensity associated with the quadratic optimization problem that needs to be solved during training. SVM classifiers were organized into a binary tree structure to speed up classification, which also reduces the total training time required. This framework was validated on composite plates that were impacted at various locations. The results show that the algorithm was able to correctly predict the different impact damage cases in composite laminates using less than 21 percent of the total available training data after data reduction.

Original languageEnglish (US)
Title of host publicationModeling, Signal Processing, and Control for Smart Structures 2009
StatePublished - 2009
EventModeling, Signal Processing, and Control for Smart Structures 2009 - San Diego, CA, United States
Duration: Mar 11 2009Mar 12 2009

Publication series

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


OtherModeling, Signal Processing, and Control for Smart Structures 2009
Country/TerritoryUnited States
CitySan Diego, CA


  • Binary tree
  • Carbon fiber composite
  • Damage classification
  • Feature reduction
  • Impact
  • Linear discriminant analysis
  • Structural health monitoring
  • Support vector machines

ASJC Scopus subject areas

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


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