Details
| Original language | English |
|---|---|
| Title of host publication | Structural Health Monitoring 2017 |
| Subtitle of host publication | Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017 |
| Editors | Fu-Kuo Chang, Fotis Kopsaftopoulos |
| Pages | 2506-2513 |
| Number of pages | 8 |
| ISBN (electronic) | 9781605953304 |
| Publication status | Published - 2017 |
| Event | 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017 - Stanford, United States Duration: 12 Sept 2017 → 14 Sept 2017 |
Abstract
In SHM applications various damage-sensitive features can be used for making decisions regarding damage detection. In all cases, classifiers evaluate the results and make a final decision regarding the state of the structure. Often, there are discrepancies among the decisions of different classifiers, resulting in different detection performances for each damage feature. This is expected as different classifiers may be better suited for different data settings, even in data sets corresponding to the same system. Boosting algorithms combine multiple base classifiers to produce an ensemble, whose joint decision offers a better performance than any of the base classifiers. Adaptive Boosting (AdaBoost) is deployed in this paper to build a strong classifier based on the classifiers of a three-tier modular SHM framework for improving detection performance. The framework consists of three parts: application of machine learning clustering algorithms for data normalization, feature extraction and hypothesis testing (HT). Each connection of damage feature, also referred to as condition parameter (CP), and HT composes a classifier that can be used as a weak classifier in the boosting algorithm. Information from the SHM framework classifiers is used, in order to build a strong classifier that is able to classify the value of any CP and improve the detection performance. The integration of AdaBoost with the three-tier SHM framework is validated on an operating 3 kW wind turbine. The results are demonstrated in receiver operating characteristic (ROC) curves with AdaBoost increasing the performance of damage detection.
ASJC Scopus subject areas
- Health Professions(all)
- Health Information Management
- Computer Science(all)
- Computer Science Applications
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Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017. ed. / Fu-Kuo Chang; Fotis Kopsaftopoulos. 2017. p. 2506-2513.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Improvement of the damage detection performance of a SHM framework by using AdaBoost
T2 - 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017
AU - Tsiapoki, Stavroula
AU - Lynch, Jerome P.
AU - Kane, Michael
AU - Rolfes, Raimund
N1 - Funding information: The authors gratefully acknowledge the support of the National Science Foundation under Grants CMMI-1362513 and CMMI-1436631.
PY - 2017
Y1 - 2017
N2 - In SHM applications various damage-sensitive features can be used for making decisions regarding damage detection. In all cases, classifiers evaluate the results and make a final decision regarding the state of the structure. Often, there are discrepancies among the decisions of different classifiers, resulting in different detection performances for each damage feature. This is expected as different classifiers may be better suited for different data settings, even in data sets corresponding to the same system. Boosting algorithms combine multiple base classifiers to produce an ensemble, whose joint decision offers a better performance than any of the base classifiers. Adaptive Boosting (AdaBoost) is deployed in this paper to build a strong classifier based on the classifiers of a three-tier modular SHM framework for improving detection performance. The framework consists of three parts: application of machine learning clustering algorithms for data normalization, feature extraction and hypothesis testing (HT). Each connection of damage feature, also referred to as condition parameter (CP), and HT composes a classifier that can be used as a weak classifier in the boosting algorithm. Information from the SHM framework classifiers is used, in order to build a strong classifier that is able to classify the value of any CP and improve the detection performance. The integration of AdaBoost with the three-tier SHM framework is validated on an operating 3 kW wind turbine. The results are demonstrated in receiver operating characteristic (ROC) curves with AdaBoost increasing the performance of damage detection.
AB - In SHM applications various damage-sensitive features can be used for making decisions regarding damage detection. In all cases, classifiers evaluate the results and make a final decision regarding the state of the structure. Often, there are discrepancies among the decisions of different classifiers, resulting in different detection performances for each damage feature. This is expected as different classifiers may be better suited for different data settings, even in data sets corresponding to the same system. Boosting algorithms combine multiple base classifiers to produce an ensemble, whose joint decision offers a better performance than any of the base classifiers. Adaptive Boosting (AdaBoost) is deployed in this paper to build a strong classifier based on the classifiers of a three-tier modular SHM framework for improving detection performance. The framework consists of three parts: application of machine learning clustering algorithms for data normalization, feature extraction and hypothesis testing (HT). Each connection of damage feature, also referred to as condition parameter (CP), and HT composes a classifier that can be used as a weak classifier in the boosting algorithm. Information from the SHM framework classifiers is used, in order to build a strong classifier that is able to classify the value of any CP and improve the detection performance. The integration of AdaBoost with the three-tier SHM framework is validated on an operating 3 kW wind turbine. The results are demonstrated in receiver operating characteristic (ROC) curves with AdaBoost increasing the performance of damage detection.
UR - http://www.scopus.com/inward/record.url?scp=85032356867&partnerID=8YFLogxK
U2 - 10.12783/shm2017/14149
DO - 10.12783/shm2017/14149
M3 - Conference contribution
AN - SCOPUS:85032356867
SP - 2506
EP - 2513
BT - Structural Health Monitoring 2017
A2 - Chang, Fu-Kuo
A2 - Kopsaftopoulos, Fotis
Y2 - 12 September 2017 through 14 September 2017
ER -