Data mining scheme for the characterization of AE signals in steel elements subject to fatigue cracking

Felipe Mejia, Navid Nemati, Antonio Nanni

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

Abstract

Early detection of fatigue crack-growth in steel structures is an ongoing challenge. Furthermore, characterization of the different stages of the fatigue lifecycle using NDE techniques is particularly difficult. AE systems have been shown to serve as early damage detection mechanisms in bridge structures. This technology, however, is fraught with noise problems and complex datasets that are difficult to interpret. This paper attempts to design and implement a data mining scheme that can classify raw AE datasets into discrete clusters using an improved variant of the popular k-means clustering algorithm. The datasets are then augmented with the class label found during clustering, and a series of rules are inferred using a C4.5 decision tree classification algorithm. An implementation of the data mining scheme is coded in MATLAB®, with data from PAC® AE systems as the input. In order to validate this procedure, data from a pencil lead break test with a concurrent noise source is fed into the data mining program. Classification using the decision tree is compared to manual classification of the pencil lead break hits. The resulting decision tree is then applied to a similar dataset in order to evaluate the generality of the resulting rule sets. Once validated, the data mining program is applied to data belonging to a steel fatigue crack-growth test. Results of this classification are discussed, and possible improvements to the data mining scheme are suggested.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
DOIs
StatePublished - May 15 2012
EventNondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2012 - San Diego, CA, United States
Duration: Mar 12 2012Mar 15 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8347
ISSN (Print)0277-786X

Other

OtherNondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2012
CountryUnited States
CitySan Diego, CA
Period3/12/123/15/12

Keywords

  • Acoustic emission
  • Crack-growth
  • Data mining
  • Fatigue
  • Structural health monitoring

ASJC Scopus subject areas

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

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  • Cite this

    Mejia, F., Nemati, N., & Nanni, A. (2012). Data mining scheme for the characterization of AE signals in steel elements subject to fatigue cracking. In Proceedings of SPIE - The International Society for Optical Engineering [83471S] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8347). https://doi.org/10.1117/12.915383