| Residential College | false |
| Status | 已發表Published |
| Streaming variational inference-empowered Bayesian nonparametric clustering for online structural damage detection with transmissibility function | |
Mei, Ling Feng1; Yan, Wang Ji1,2 ; Yuen, Ka Veng1,2; Beer, Michael3,4,5
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| 2024-07-22 | |
| Source Publication | Mechanical Systems and Signal Processing
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| ISSN | 0888-3270 |
| Volume | 222 |
| Abstract | Transmissibility function (TF) is widely applied in damage detection due to its sensitivity to damage and robustness to external excitations, but its application in online damage detection is rarely reported due to challenges in handling data streams. This study proposes a new TF-based online damage detection method that integrates a truncation-free variational inference-based full Dirichlet process Gaussian mixture model (VI-FDPGMM) within a streaming variational inference (SVI) paradigm. As an improved Bayesian nonparametric approach, the truncation-free VI-FDPGMM addresses the issue of truncating mixing components in traditional VI-DPGMM for online learning with increasing data by strategically setting the variational distributions of parameters for the components without assigned data (i.e., inactivated components) to their prior distributions based on the Bayesian viewpoint, which enables computing the probabilities to assign data points to these components and determining the creation of new components. As a result, the truncation-free VI-FDPGMM allows dynamically adding components to the mixture model, providing the flexibility to automatically adapt the number of components for arbitrary amounts of data. This characteristic enables its intuitive integration into the SVI paradigm featured as the variational posterior conditioned on the previous data as the prior when new data are observed, facilitating continuous refinement of the mixture model without repeatedly making inference of previous data. Therefore, the proposed method is highly efficient and well-suited for online damage detection. The proposed method is validated using two case studies, demonstrating its capability to dynamically generate new clusters as new data are available online to indicate the emergence of new damage patterns during the monitoring process, which enables it to perform structural anomaly detection tasks in a semi-supervised manner. Furthermore, the method outperforms some state-of-the-art methods due to its capability for continuous model refinement and robustness in interpreting and capturing uncertainties. |
| Keyword | Bayesian Nonparametric Model Damage Detection Streaming Variational Inference Truncation-free Full Dirichlet Process Gaussian Mixture Model |
| DOI | 10.1016/j.ymssp.2024.111767 |
| URL | View the original |
| Language | 英語English |
| Scopus ID | 2-s2.0-85200435797 |
| Fulltext Access | |
| Citation statistics | |
| Document Type | Journal article |
| Collection | THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU) DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING |
| Affiliation | 1.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, China 2.Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, China 3.Leibniz Universität Hannover, Institute for Risk and Reliability, Hannover, Germany 4.University of Liverpool, Institute for Risk and Uncertainty, Liverpool, United Kingdom 5.Tsinghua University, Department of Civil Engineering, Beijing, China |
| First Author Affilication | University of Macau |
| Recommended Citation GB/T 7714 | Mei, Ling Feng,Yan, Wang Ji,Yuen, Ka Veng,et al. Streaming variational inference-empowered Bayesian nonparametric clustering for online structural damage detection with transmissibility function[J]. Mechanical Systems and Signal Processing, 2024, 222. |
| APA | Mei, Ling Feng., Yan, Wang Ji., Yuen, Ka Veng., & Beer, Michael (2024). Streaming variational inference-empowered Bayesian nonparametric clustering for online structural damage detection with transmissibility function. Mechanical Systems and Signal Processing, 222. |
| MLA | Mei, Ling Feng,et al."Streaming variational inference-empowered Bayesian nonparametric clustering for online structural damage detection with transmissibility function".Mechanical Systems and Signal Processing 222(2024). |
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