Deep Learning-based Methods for Structural Health Monitoring Data Improvement and Augmentation
This thesis addresses data quality challenges in Structural Health Monitoring (SHM) using deep learning techniques. A Transformer-based generative adversarial network is developed to reconstruct missing signal. An unsupervised domain adaptation-based methodology is proposed to impute missing data. A...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/97309 |
| _version_ | 1848766251438964736 |
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| author | Zheng, Wenhao |
| author_facet | Zheng, Wenhao |
| author_sort | Zheng, Wenhao |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis addresses data quality challenges in Structural Health Monitoring (SHM) using deep learning techniques. A Transformer-based generative adversarial network is developed to reconstruct missing signal. An unsupervised domain adaptation-based methodology is proposed to impute missing data. A segmentation method for detecting anomalous data is developed by employing denoising diffusion probabilistic models (DDPMs). Additionally, a generative model using DDPMs is proposed to synthesize realistic monitoring data, enhancing reconstruction accuracy and data augmentation in SHM applications. |
| first_indexed | 2025-11-14T11:48:10Z |
| format | Thesis |
| id | curtin-20.500.11937-97309 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:48:10Z |
| publishDate | 2024 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-973092025-03-10T23:59:40Z Deep Learning-based Methods for Structural Health Monitoring Data Improvement and Augmentation Zheng, Wenhao This thesis addresses data quality challenges in Structural Health Monitoring (SHM) using deep learning techniques. A Transformer-based generative adversarial network is developed to reconstruct missing signal. An unsupervised domain adaptation-based methodology is proposed to impute missing data. A segmentation method for detecting anomalous data is developed by employing denoising diffusion probabilistic models (DDPMs). Additionally, a generative model using DDPMs is proposed to synthesize realistic monitoring data, enhancing reconstruction accuracy and data augmentation in SHM applications. 2024 Thesis http://hdl.handle.net/20.500.11937/97309 Curtin University restricted |
| spellingShingle | Zheng, Wenhao Deep Learning-based Methods for Structural Health Monitoring Data Improvement and Augmentation |
| title | Deep Learning-based Methods for Structural Health Monitoring Data Improvement and Augmentation |
| title_full | Deep Learning-based Methods for Structural Health Monitoring Data Improvement and Augmentation |
| title_fullStr | Deep Learning-based Methods for Structural Health Monitoring Data Improvement and Augmentation |
| title_full_unstemmed | Deep Learning-based Methods for Structural Health Monitoring Data Improvement and Augmentation |
| title_short | Deep Learning-based Methods for Structural Health Monitoring Data Improvement and Augmentation |
| title_sort | deep learning-based methods for structural health monitoring data improvement and augmentation |
| url | http://hdl.handle.net/20.500.11937/97309 |