Details
| Original language | English |
|---|---|
| Article number | 7509630 |
| Pages (from-to) | 1632-1646 |
| Number of pages | 15 |
| Journal | Proceedings of the IEEE |
| Volume | 104 |
| Issue number | 8 |
| Publication status | Published - Aug 2016 |
Abstract
This paper proposes a three-Tier algorithmic framework as the basis for the flexible design of data-driven structural health monitoring (SHM) systems. The three major functions of the SHM system, including data normalization, feature extraction, and hypothesis testing (HT), are mapped to the three layers of the framework. The first tier of the framework is devoted to data normalization. Machine learning (ML) methods are adopted to normalize available data sets by binning data sets to similar environmental and operational conditions (EOCs) of the system. Specifically, affinity propagation clustering is used to delineate data into groups of similar EOC. Once data are normalized by EOC, the second tier of the framework extracts features from the data to serve as condition parameters (CPs) for damage assessment. To ascertain the health state of the structure, the third tier of the framework is devoted to statistical analysis of the CP through HT. An intrinsic goal of the study is to explore the modularity of the three tier framework as a means of offering SHM system designers opportunity to explore and test different computational block sets at each layer to maximize the detection capability of the SHM system. Various realizations of the three-Tier modular framework are presented and applied to acceleration and EOC data collected from an operational 3-kW wind turbine. In total, 354 data sets are collected from the turbine, including tower lateral accelerations in two orthogonal directions at six heights, wind speed and wind direction; 317 of the data sets correspond to the wind turbine in a healthy state and 37 with the wind turbine in a damage state. Using quantitative metrics derived from receiver operating characteristic (ROC) curves, the damage classification capabilities of the framework are validated and shown to accurately identify intentionally introduced damage in the turbine.
Keywords
- Condition parameters (CPs), hypothesis testing (HT), machine learning (ML), parameter estimation, probability, statistical analysis, structural health monitoring (SHM), wind turbines
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
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In: Proceedings of the IEEE, Vol. 104, No. 8, 7509630, 08.2016, p. 1632-1646.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Three-Tier Modular Structural Health Monitoring Framework Using Environmental and Operational Condition Clustering for Data Normalization
T2 - Validation on an Operational Wind Turbine System
AU - Häckell, Moritz W.
AU - Rolfes, Raimund
AU - Kane, Michael B.
AU - Lynch, Jerome P.
N1 - Funding information: U.S. National Science Foundation under Grants CMMI-1362513, CMMI-1436631
PY - 2016/8
Y1 - 2016/8
N2 - This paper proposes a three-Tier algorithmic framework as the basis for the flexible design of data-driven structural health monitoring (SHM) systems. The three major functions of the SHM system, including data normalization, feature extraction, and hypothesis testing (HT), are mapped to the three layers of the framework. The first tier of the framework is devoted to data normalization. Machine learning (ML) methods are adopted to normalize available data sets by binning data sets to similar environmental and operational conditions (EOCs) of the system. Specifically, affinity propagation clustering is used to delineate data into groups of similar EOC. Once data are normalized by EOC, the second tier of the framework extracts features from the data to serve as condition parameters (CPs) for damage assessment. To ascertain the health state of the structure, the third tier of the framework is devoted to statistical analysis of the CP through HT. An intrinsic goal of the study is to explore the modularity of the three tier framework as a means of offering SHM system designers opportunity to explore and test different computational block sets at each layer to maximize the detection capability of the SHM system. Various realizations of the three-Tier modular framework are presented and applied to acceleration and EOC data collected from an operational 3-kW wind turbine. In total, 354 data sets are collected from the turbine, including tower lateral accelerations in two orthogonal directions at six heights, wind speed and wind direction; 317 of the data sets correspond to the wind turbine in a healthy state and 37 with the wind turbine in a damage state. Using quantitative metrics derived from receiver operating characteristic (ROC) curves, the damage classification capabilities of the framework are validated and shown to accurately identify intentionally introduced damage in the turbine.
AB - This paper proposes a three-Tier algorithmic framework as the basis for the flexible design of data-driven structural health monitoring (SHM) systems. The three major functions of the SHM system, including data normalization, feature extraction, and hypothesis testing (HT), are mapped to the three layers of the framework. The first tier of the framework is devoted to data normalization. Machine learning (ML) methods are adopted to normalize available data sets by binning data sets to similar environmental and operational conditions (EOCs) of the system. Specifically, affinity propagation clustering is used to delineate data into groups of similar EOC. Once data are normalized by EOC, the second tier of the framework extracts features from the data to serve as condition parameters (CPs) for damage assessment. To ascertain the health state of the structure, the third tier of the framework is devoted to statistical analysis of the CP through HT. An intrinsic goal of the study is to explore the modularity of the three tier framework as a means of offering SHM system designers opportunity to explore and test different computational block sets at each layer to maximize the detection capability of the SHM system. Various realizations of the three-Tier modular framework are presented and applied to acceleration and EOC data collected from an operational 3-kW wind turbine. In total, 354 data sets are collected from the turbine, including tower lateral accelerations in two orthogonal directions at six heights, wind speed and wind direction; 317 of the data sets correspond to the wind turbine in a healthy state and 37 with the wind turbine in a damage state. Using quantitative metrics derived from receiver operating characteristic (ROC) curves, the damage classification capabilities of the framework are validated and shown to accurately identify intentionally introduced damage in the turbine.
KW - Condition parameters (CPs)
KW - hypothesis testing (HT)
KW - machine learning (ML)
KW - parameter estimation
KW - probability
KW - statistical analysis
KW - structural health monitoring (SHM)
KW - wind turbines
UR - http://www.scopus.com/inward/record.url?scp=84978289193&partnerID=8YFLogxK
U2 - 10.1109/JPROC.2016.2566602
DO - 10.1109/JPROC.2016.2566602
M3 - Article
AN - SCOPUS:84978289193
VL - 104
SP - 1632
EP - 1646
JO - Proceedings of the IEEE
JF - Proceedings of the IEEE
SN - 0018-9219
IS - 8
M1 - 7509630
ER -