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 language | English (US) |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 6926 |
| DOIs | |
| State | Published - 2008 |
| Event | Modeling, Signal Processing, and Control for Smart Structures 2008 - San Diego, CA, United States Duration: Mar 10 2008 → Mar 12 2008 |
Other
| Other | Modeling, Signal Processing, and Control for Smart Structures 2008 |
|---|---|
| Country | United States |
| City | San Diego, CA |
| Period | 3/10/08 → 3/12/08 |
Fingerprint
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
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 proceeding › Conference contribution
}
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 -