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A platform for research: civil engineering, architecture and urbanism
Structural Health Monitoring of Offshore Jacket Platforms via Transformers
https://orcid.org/http://orcid.org/0000-0001-6322-4608
https://orcid.org/http://orcid.org/0000-0002-4018-6319
https://orcid.org/http://orcid.org/0000-0003-4964-6948
https://orcid.org/http://orcid.org/0000-0003-0610-089X
The goal of this project is to monitor the structural health of jacket-type platforms for offshore wind turbines. The methodology is based on vibration-response-only accelerometer measurement and a transformer-based framework for multivariate time series. The original transformers paper proposed an architecture applied to a natural language processing task, meanwhile later works approached the use of transformers for forecasting, missing value imputation, and classification of time series. In general, the transformers based on attention mechanisms demonstrate being superior in terms of quality and performance on many sequential tasks in comparison to other architectures. Similar results are expected with time series data. Thus, this work proposes to use transformers for the classification of different structural types of damage in jacket-type wind turbines. The methodology follows the next steps: (i) accelerometer data is acquired, (ii) data is cleaned and wrangled into time series, (iii) a transformer-based framework classifies different damage scenarios. In a down-scaled experimental laboratory structure, the method is validated. The results demonstrate the feasibility of the proposed methodology.
Structural Health Monitoring of Offshore Jacket Platforms via Transformers
https://orcid.org/http://orcid.org/0000-0001-6322-4608
https://orcid.org/http://orcid.org/0000-0002-4018-6319
https://orcid.org/http://orcid.org/0000-0003-4964-6948
https://orcid.org/http://orcid.org/0000-0003-0610-089X
The goal of this project is to monitor the structural health of jacket-type platforms for offshore wind turbines. The methodology is based on vibration-response-only accelerometer measurement and a transformer-based framework for multivariate time series. The original transformers paper proposed an architecture applied to a natural language processing task, meanwhile later works approached the use of transformers for forecasting, missing value imputation, and classification of time series. In general, the transformers based on attention mechanisms demonstrate being superior in terms of quality and performance on many sequential tasks in comparison to other architectures. Similar results are expected with time series data. Thus, this work proposes to use transformers for the classification of different structural types of damage in jacket-type wind turbines. The methodology follows the next steps: (i) accelerometer data is acquired, (ii) data is cleaned and wrangled into time series, (iii) a transformer-based framework classifies different damage scenarios. In a down-scaled experimental laboratory structure, the method is validated. The results demonstrate the feasibility of the proposed methodology.
Structural Health Monitoring of Offshore Jacket Platforms via Transformers
https://orcid.org/http://orcid.org/0000-0001-6322-4608
https://orcid.org/http://orcid.org/0000-0002-4018-6319
https://orcid.org/http://orcid.org/0000-0003-4964-6948
https://orcid.org/http://orcid.org/0000-0003-0610-089X
The goal of this project is to monitor the structural health of jacket-type platforms for offshore wind turbines. The methodology is based on vibration-response-only accelerometer measurement and a transformer-based framework for multivariate time series. The original transformers paper proposed an architecture applied to a natural language processing task, meanwhile later works approached the use of transformers for forecasting, missing value imputation, and classification of time series. In general, the transformers based on attention mechanisms demonstrate being superior in terms of quality and performance on many sequential tasks in comparison to other architectures. Similar results are expected with time series data. Thus, this work proposes to use transformers for the classification of different structural types of damage in jacket-type wind turbines. The methodology follows the next steps: (i) accelerometer data is acquired, (ii) data is cleaned and wrangled into time series, (iii) a transformer-based framework classifies different damage scenarios. In a down-scaled experimental laboratory structure, the method is validated. The results demonstrate the feasibility of the proposed methodology.
Structural Health Monitoring of Offshore Jacket Platforms via Transformers
Lecture Notes in Civil Engineering
Rizzo, Piervincenzo (editor) / Milazzo, Alberto (editor) / Tutivén, Christian (author) / Triviño, Héctor (author) / Vidal, Yolanda (author) / Sampietro, José (author)
European Workshop on Structural Health Monitoring ; 2022 ; Palermo, Italy
2022-06-19
10 pages
Article/Chapter (Book)
Electronic Resource
English
Structural health monitoring (SHM) , Offshore fixed wind turbine , Jacket structure , Damage detection , Damage classification , Vibration-based SHM , Data-driven , Transformer neural network , Multivariate , Time series Engineering , Building Repair and Maintenance , Cyber-physical systems, IoT , Industrial and Production Engineering , Monitoring/Environmental Analysis , Analytical Chemistry
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