Physics based modeling for time-frequency damage classification

Debejyo Chakraborty, Sunilkumar Soni, Jun Wei, Narayan Kovvali, Antonia Papandreou-Suppappola, Douglas Cochran, Aditi Chattopadhyay

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

5 Citations (Scopus)

Abstract

We have recently proposed a method for classifying waveforms from healthy and damaged structures in a structural health monitoring framework. This method is based on the use of hidden Markov models with preselected feature vectors obtained from the time-frequency based matching pursuit decomposition. In order to investigate the performance of the classifier for different signal-to-noise ratios (SNR), we simulate the response of a lug joint sample with different crack lengths using finite element modeling (FEM). Unlike experimental noisy data, the modeled data is noise free. As a result, different levels of noise can be added to the modeled data in order to obtain the true performance of the classifier under additive white Gaussian noise. We use the finite element package ABAQUS to simulate a lug joint sample with different crack lengths and piezoelectric sensor signals. A mesoscale internal state variable damage model defines the progressive damage and is incorporated in the macroscale model. We furthermore use a hybrid method (boundary element-finite element method) to model wave reflection as well as mode conversion of the Lamb waves from the free edges and scattering of the waves from the internal defects. The hybrid method simplifies the modeling problem and provides better performance in the analysis of high stress gradient problems.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Volume6926
DOIs
StatePublished - 2008
EventModeling, Signal Processing, and Control for Smart Structures 2008 - San Diego, CA, United States
Duration: Mar 10 2008Mar 12 2008

Other

OtherModeling, Signal Processing, and Control for Smart Structures 2008
CountryUnited States
CitySan Diego, CA
Period3/10/083/12/08

Fingerprint

lugs
Physics
damage
physics
Classifiers
classifiers
Cracks
cracks
Structural health monitoring
ABAQUS
Hidden Markov models
Boundary element method
Surface waves
wave reflection
Lamb waves
structural health monitoring
Signal to noise ratio
boundary element method
random noise
classifying

Keywords

  • Damage detection
  • Hidden Markov models
  • Matching pursuit decomposition
  • Physics based modeling
  • Structural health monitoring
  • Time-frequency analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Chakraborty, D., Soni, S., Wei, J., Kovvali, N., Papandreou-Suppappola, A., Cochran, D., & Chattopadhyay, A. (2008). Physics based modeling for time-frequency damage classification. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 6926). [69260M] https://doi.org/10.1117/12.776628

Physics based modeling for time-frequency damage classification. / Chakraborty, Debejyo; Soni, Sunilkumar; Wei, Jun; Kovvali, Narayan; Papandreou-Suppappola, Antonia; Cochran, Douglas; Chattopadhyay, Aditi.

Proceedings of SPIE - The International Society for Optical Engineering. Vol. 6926 2008. 69260M.

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

Chakraborty, D, Soni, S, Wei, J, Kovvali, N, Papandreou-Suppappola, A, Cochran, D & Chattopadhyay, A 2008, Physics based modeling for time-frequency damage classification. in Proceedings of SPIE - The International Society for Optical Engineering. vol. 6926, 69260M, Modeling, Signal Processing, and Control for Smart Structures 2008, San Diego, CA, United States, 3/10/08. https://doi.org/10.1117/12.776628
Chakraborty D, Soni S, Wei J, Kovvali N, Papandreou-Suppappola A, Cochran D et al. Physics based modeling for time-frequency damage classification. In Proceedings of SPIE - The International Society for Optical Engineering. Vol. 6926. 2008. 69260M https://doi.org/10.1117/12.776628
Chakraborty, Debejyo ; Soni, Sunilkumar ; Wei, Jun ; Kovvali, Narayan ; Papandreou-Suppappola, Antonia ; Cochran, Douglas ; Chattopadhyay, Aditi. / Physics based modeling for time-frequency damage classification. Proceedings of SPIE - The International Society for Optical Engineering. Vol. 6926 2008.
@inproceedings{2c26b34916354c3b89f65312893a093c,
title = "Physics based modeling for time-frequency damage classification",
abstract = "We have recently proposed a method for classifying waveforms from healthy and damaged structures in a structural health monitoring framework. This method is based on the use of hidden Markov models with preselected feature vectors obtained from the time-frequency based matching pursuit decomposition. In order to investigate the performance of the classifier for different signal-to-noise ratios (SNR), we simulate the response of a lug joint sample with different crack lengths using finite element modeling (FEM). Unlike experimental noisy data, the modeled data is noise free. As a result, different levels of noise can be added to the modeled data in order to obtain the true performance of the classifier under additive white Gaussian noise. We use the finite element package ABAQUS to simulate a lug joint sample with different crack lengths and piezoelectric sensor signals. A mesoscale internal state variable damage model defines the progressive damage and is incorporated in the macroscale model. We furthermore use a hybrid method (boundary element-finite element method) to model wave reflection as well as mode conversion of the Lamb waves from the free edges and scattering of the waves from the internal defects. The hybrid method simplifies the modeling problem and provides better performance in the analysis of high stress gradient problems.",
keywords = "Damage detection, Hidden Markov models, Matching pursuit decomposition, Physics based modeling, Structural health monitoring, Time-frequency analysis",
author = "Debejyo Chakraborty and Sunilkumar Soni and Jun Wei and Narayan Kovvali and Antonia Papandreou-Suppappola and Douglas Cochran and Aditi Chattopadhyay",
year = "2008",
doi = "10.1117/12.776628",
language = "English (US)",
isbn = "9780819471123",
volume = "6926",
booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

}

TY - GEN

T1 - Physics based modeling for time-frequency damage classification

AU - Chakraborty, Debejyo

AU - Soni, Sunilkumar

AU - Wei, Jun

AU - Kovvali, Narayan

AU - Papandreou-Suppappola, Antonia

AU - Cochran, Douglas

AU - Chattopadhyay, Aditi

PY - 2008

Y1 - 2008

N2 - We have recently proposed a method for classifying waveforms from healthy and damaged structures in a structural health monitoring framework. This method is based on the use of hidden Markov models with preselected feature vectors obtained from the time-frequency based matching pursuit decomposition. In order to investigate the performance of the classifier for different signal-to-noise ratios (SNR), we simulate the response of a lug joint sample with different crack lengths using finite element modeling (FEM). Unlike experimental noisy data, the modeled data is noise free. As a result, different levels of noise can be added to the modeled data in order to obtain the true performance of the classifier under additive white Gaussian noise. We use the finite element package ABAQUS to simulate a lug joint sample with different crack lengths and piezoelectric sensor signals. A mesoscale internal state variable damage model defines the progressive damage and is incorporated in the macroscale model. We furthermore use a hybrid method (boundary element-finite element method) to model wave reflection as well as mode conversion of the Lamb waves from the free edges and scattering of the waves from the internal defects. The hybrid method simplifies the modeling problem and provides better performance in the analysis of high stress gradient problems.

AB - We have recently proposed a method for classifying waveforms from healthy and damaged structures in a structural health monitoring framework. This method is based on the use of hidden Markov models with preselected feature vectors obtained from the time-frequency based matching pursuit decomposition. In order to investigate the performance of the classifier for different signal-to-noise ratios (SNR), we simulate the response of a lug joint sample with different crack lengths using finite element modeling (FEM). Unlike experimental noisy data, the modeled data is noise free. As a result, different levels of noise can be added to the modeled data in order to obtain the true performance of the classifier under additive white Gaussian noise. We use the finite element package ABAQUS to simulate a lug joint sample with different crack lengths and piezoelectric sensor signals. A mesoscale internal state variable damage model defines the progressive damage and is incorporated in the macroscale model. We furthermore use a hybrid method (boundary element-finite element method) to model wave reflection as well as mode conversion of the Lamb waves from the free edges and scattering of the waves from the internal defects. The hybrid method simplifies the modeling problem and provides better performance in the analysis of high stress gradient problems.

KW - Damage detection

KW - Hidden Markov models

KW - Matching pursuit decomposition

KW - Physics based modeling

KW - Structural health monitoring

KW - Time-frequency analysis

UR - http://www.scopus.com/inward/record.url?scp=44449116699&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=44449116699&partnerID=8YFLogxK

U2 - 10.1117/12.776628

DO - 10.1117/12.776628

M3 - Conference contribution

AN - SCOPUS:44449116699

SN - 9780819471123

VL - 6926

BT - Proceedings of SPIE - The International Society for Optical Engineering

ER -