A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis
This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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The Prognostics and Health Management Society
2017-06-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: | |
| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/2637 |
| _version_ | 1818617782696148992 |
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| author | James E. Warner Geoffrey F. Bomarito Jacob D. Hochhalter William P. Leser Patrick E. Leser John A. Newman |
| author_facet | James E. Warner Geoffrey F. Bomarito Jacob D. Hochhalter William P. Leser Patrick E. Leser John A. Newman |
| author_sort | James E. Warner |
| collection | DOAJ |
| description | This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model. |
| first_indexed | 2024-12-16T17:11:10Z |
| format | Article |
| id | doaj.art-555c567dae0b48db9da296cce83d20b1 |
| institution | Directory Open Access Journal |
| issn | 2153-2648 2153-2648 |
| language | English |
| last_indexed | 2024-12-16T17:11:10Z |
| publishDate | 2017-06-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj.art-555c567dae0b48db9da296cce83d20b12022-12-21T22:23:24ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482153-26482017-06-0182doi:10.36001/ijphm.2017.v8i2.2637A Computationally-Efficient Probabilistic Approach to Model-Based Damage DiagnosisJames E. Warner0Geoffrey F. Bomarito1Jacob D. Hochhalter2William P. Leser3Patrick E. Leser4John A. Newman5NASA Langley Research Center, Hampton, VA, 23666, USANASA Langley Research Center, Hampton, VA, 23666, USANASA Langley Research Center, Hampton, VA, 23666, USANASA Langley Research Center, Hampton, VA, 23666, USANASA Langley Research Center, Hampton, VA, 23666, USANASA Langley Research Center, Hampton, VA, 23666, USAThis work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model.https://papers.phmsociety.org/index.php/ijphm/article/view/2637uncertainty quantificationsurrogate modelingdamage diagnosis |
| spellingShingle | James E. Warner Geoffrey F. Bomarito Jacob D. Hochhalter William P. Leser Patrick E. Leser John A. Newman A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis International Journal of Prognostics and Health Management uncertainty quantification surrogate modeling damage diagnosis |
| title | A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis |
| title_full | A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis |
| title_fullStr | A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis |
| title_full_unstemmed | A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis |
| title_short | A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis |
| title_sort | computationally efficient probabilistic approach to model based damage diagnosis |
| topic | uncertainty quantification surrogate modeling damage diagnosis |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/2637 |
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