Damage classification for structural health monitoring using time-frequency feature extraction and continuous hidden Markov models

W. Zhou; D. Chakraborty; N. Kovvali; Antonia Papandreou-Suppappola; Douglas Cochran; Aditi Chattopadhyay

doi:10.1109/ACSSC.2007.4487337

Damage classification for structural health monitoring using time-frequency feature extraction and continuous hidden Markov models

W. Zhou, D. Chakraborty, N. Kovvali, Antonia Papandreou-Suppappola, Douglas Cochran, Aditi Chattopadhyay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Scopus citations

Abstract

We propose an algorithm for the classification of structural damage based on the use of the continuous hidden Markov modeling (HMM) technique. Our approach employs HMMs to model time-frequency damage features extracted from structural data using the matching pursuit decomposition algorithm. We investigate modeling with continuous observation-density HMMs and discuss the trade-offs involved as compared to the discrete HMM case. A variational Bayesian method is employed to automatically estimate the HMM state number and adapt the classifier for real-time use. We present results that classify structural and material (fatigue) damage in a boltedjoint structure.

Original language	English (US)
Title of host publication	Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Pages	848-852
Number of pages	5
DOIs	https://doi.org/10.1109/ACSSC.2007.4487337
State	Published - 2007
Event	41st Asilomar Conference on Signals, Systems and Computers, ACSSC - Pacific Grove, CA, United States Duration: Nov 4 2007 → Nov 7 2007

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Country/Territory	United States
City	Pacific Grove, CA
Period	11/4/07 → 11/7/07

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

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10.1109/ACSSC.2007.4487337

Cite this

Zhou, W., Chakraborty, D., Kovvali, N., Papandreou-Suppappola, A., Cochran, D., & Chattopadhyay, A. (2007). Damage classification for structural health monitoring using time-frequency feature extraction and continuous hidden Markov models. In Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC (pp. 848-852). [4487337] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2007.4487337

Damage classification for structural health monitoring using time-frequency feature extraction and continuous hidden Markov models. / Zhou, W.; Chakraborty, D.; Kovvali, N. et al.

Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC. 2007. p. 848-852 4487337 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhou, W, Chakraborty, D, Kovvali, N, Papandreou-Suppappola, A , Cochran, D & Chattopadhyay, A 2007, Damage classification for structural health monitoring using time-frequency feature extraction and continuous hidden Markov models. in Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC., 4487337, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 848-852, 41st Asilomar Conference on Signals, Systems and Computers, ACSSC, Pacific Grove, CA, United States, 11/4/07. https://doi.org/10.1109/ACSSC.2007.4487337

Zhou W, Chakraborty D, Kovvali N, Papandreou-Suppappola A , Cochran D , Chattopadhyay A. Damage classification for structural health monitoring using time-frequency feature extraction and continuous hidden Markov models. In Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC. 2007. p. 848-852. 4487337. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2007.4487337

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abstract = "We propose an algorithm for the classification of structural damage based on the use of the continuous hidden Markov modeling (HMM) technique. Our approach employs HMMs to model time-frequency damage features extracted from structural data using the matching pursuit decomposition algorithm. We investigate modeling with continuous observation-density HMMs and discuss the trade-offs involved as compared to the discrete HMM case. A variational Bayesian method is employed to automatically estimate the HMM state number and adapt the classifier for real-time use. We present results that classify structural and material (fatigue) damage in a boltedjoint structure.",

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AB - We propose an algorithm for the classification of structural damage based on the use of the continuous hidden Markov modeling (HMM) technique. Our approach employs HMMs to model time-frequency damage features extracted from structural data using the matching pursuit decomposition algorithm. We investigate modeling with continuous observation-density HMMs and discuss the trade-offs involved as compared to the discrete HMM case. A variational Bayesian method is employed to automatically estimate the HMM state number and adapt the classifier for real-time use. We present results that classify structural and material (fatigue) damage in a boltedjoint structure.

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Damage classification for structural health monitoring using time-frequency feature extraction and continuous hidden Markov models

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