Details
| Original language | English |
|---|---|
| Article number | 110702 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 203 |
| Early online date | 31 Aug 2023 |
| Publication status | Published - 15 Nov 2023 |
Abstract
This paper proposes a new damage detection method by integrating the advantage of transmissibility function (TF) as a health index sensitive to damage but robust to excitation and agglomerative hierarchical clustering (AHC) with intuitive explanation and visualization but avoiding specifying the number of clusters. Different from conventional AHC-based damage detection methods utilizing deterministic distance as a similarity metric and ignoring the distribution of structural features, a multivariate probabilistic distance-based similarity metric is proposed in this study to account for the uncertainty and correlation of multiple TFs following multivariate complex-valued Gaussian ratio distribution. To realize this, an analytically tractable approximation of the multivariate probabilistic distance is derived by Laplace's asymptotic expansion to avoid high-dimensional numerical integration. To accelerate the computation of probabilistic distances over a wide frequency band that are fused to formulate the similarity metric in AHC, a function vectorization scheme is proposed to avoid the time-consuming loop operation among different frequency points. A threshold is established via bootstrapped Monte Carlo simulation to cut the dendrogram produced by AHC. Two case studies are used to validate the performance of the proposed method, indicating that, compared to the damage detection methods based on the deterministic distance of the TF, the proposed method exhibits better performance due to improving the similarity metric based on multivariate probabilistic distance properly accommodating the correlation of different TFs.
Keywords
- Damage detection, Hierarchical clustering, Multivariate distribution, Probabilistic distance, Transmissibility
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 203, 110702, 15.11.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Transmissibility-based damage detection with hierarchical clustering enhanced by multivariate probabilistic distance accommodating uncertainty and correlation
AU - Mei, Lin Feng
AU - Yan, Wang Ji
AU - Yuen, Ka Veng
AU - Ren, Wei Xin
AU - Beer, Michael
N1 - Funding Information: This research has been supported by the Science and Technology Development Fund, Macau SAR (File no.: 017/2020/A1, 101/2021/A2, 0010/2021/AGJ, SKL-IOTSC(UM)-2021-2023), the Research Committee of University of Macau (File no.: MYRG2020-00073-IOTSC and MYRG2022-00096-IOTSC), Guangdong-Hong Kong-Macau Joint Laboratory Program (Project No.: 2020B1212030009) and Shenzhen Science and Technology Program (Grant Nos. JSGG20210802093207022, KQTD20180412181337494 and ZDSYS20201020162400001). Also, the authors are deeply appreciative to professors at the University of Leuven in Belgium for providing the experimental datasets of the benchmark structure.
PY - 2023/11/15
Y1 - 2023/11/15
N2 - This paper proposes a new damage detection method by integrating the advantage of transmissibility function (TF) as a health index sensitive to damage but robust to excitation and agglomerative hierarchical clustering (AHC) with intuitive explanation and visualization but avoiding specifying the number of clusters. Different from conventional AHC-based damage detection methods utilizing deterministic distance as a similarity metric and ignoring the distribution of structural features, a multivariate probabilistic distance-based similarity metric is proposed in this study to account for the uncertainty and correlation of multiple TFs following multivariate complex-valued Gaussian ratio distribution. To realize this, an analytically tractable approximation of the multivariate probabilistic distance is derived by Laplace's asymptotic expansion to avoid high-dimensional numerical integration. To accelerate the computation of probabilistic distances over a wide frequency band that are fused to formulate the similarity metric in AHC, a function vectorization scheme is proposed to avoid the time-consuming loop operation among different frequency points. A threshold is established via bootstrapped Monte Carlo simulation to cut the dendrogram produced by AHC. Two case studies are used to validate the performance of the proposed method, indicating that, compared to the damage detection methods based on the deterministic distance of the TF, the proposed method exhibits better performance due to improving the similarity metric based on multivariate probabilistic distance properly accommodating the correlation of different TFs.
AB - This paper proposes a new damage detection method by integrating the advantage of transmissibility function (TF) as a health index sensitive to damage but robust to excitation and agglomerative hierarchical clustering (AHC) with intuitive explanation and visualization but avoiding specifying the number of clusters. Different from conventional AHC-based damage detection methods utilizing deterministic distance as a similarity metric and ignoring the distribution of structural features, a multivariate probabilistic distance-based similarity metric is proposed in this study to account for the uncertainty and correlation of multiple TFs following multivariate complex-valued Gaussian ratio distribution. To realize this, an analytically tractable approximation of the multivariate probabilistic distance is derived by Laplace's asymptotic expansion to avoid high-dimensional numerical integration. To accelerate the computation of probabilistic distances over a wide frequency band that are fused to formulate the similarity metric in AHC, a function vectorization scheme is proposed to avoid the time-consuming loop operation among different frequency points. A threshold is established via bootstrapped Monte Carlo simulation to cut the dendrogram produced by AHC. Two case studies are used to validate the performance of the proposed method, indicating that, compared to the damage detection methods based on the deterministic distance of the TF, the proposed method exhibits better performance due to improving the similarity metric based on multivariate probabilistic distance properly accommodating the correlation of different TFs.
KW - Damage detection
KW - Hierarchical clustering
KW - Multivariate distribution
KW - Probabilistic distance
KW - Transmissibility
UR - http://www.scopus.com/inward/record.url?scp=85169977391&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2023.110702
DO - 10.1016/j.ymssp.2023.110702
M3 - Article
AN - SCOPUS:85169977391
VL - 203
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 110702
ER -