Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement
A new digital twin (DT) framework with optimal sensor placement (OSP) is proposed to accurately calculate the modal responses and identify the damage ratios of the offshore jacket platforms. The proposed damage identification framework consists of two models (namely one OSP model and one damage iden...
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| Vydané v: | Reliability engineering & system safety Ročník 237; s. 109336 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.09.2023
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| ISSN: | 0951-8320, 1879-0836 |
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| Abstract | A new digital twin (DT) framework with optimal sensor placement (OSP) is proposed to accurately calculate the modal responses and identify the damage ratios of the offshore jacket platforms. The proposed damage identification framework consists of two models (namely one OSP model and one damage identification model). The OSP model adopts the multi-objective Lichtenberg algorithm (MOLA) to perform the sensor number/location optimization to make a good balance between the sensor cost and the modal calculation accuracy. In the damage identification model, the Markov Chain Monte Carlo (MCMC)-Bayesian method is developed to calculate the structural damage ratios based on the modal information obtained from the sensory measurements, where the uncertainties of the structural parameters are quantified. The proposed method is validated using an offshore jacket platform, and the analysis results demonstrate efficient identification of the structural damage location and severity. |
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| AbstractList | A new digital twin (DT) framework with optimal sensor placement (OSP) is proposed to accurately calculate the modal responses and identify the damage ratios of the offshore jacket platforms. The proposed damage identification framework consists of two models (namely one OSP model and one damage identification model). The OSP model adopts the multi-objective Lichtenberg algorithm (MOLA) to perform the sensor number/location optimization to make a good balance between the sensor cost and the modal calculation accuracy. In the damage identification model, the Markov Chain Monte Carlo (MCMC)-Bayesian method is developed to calculate the structural damage ratios based on the modal information obtained from the sensory measurements, where the uncertainties of the structural parameters are quantified. The proposed method is validated using an offshore jacket platform, and the analysis results demonstrate efficient identification of the structural damage location and severity. |
| ArticleNumber | 109336 |
| Author | Gupta, M.K. Królczyk, Grzegorz Incecik, Atilla Feng, Shizhe Li, Z Wang, Mengmeng |
| Author_xml | – sequence: 1 givenname: Mengmeng surname: Wang fullname: Wang, Mengmeng organization: Department of Marine Engineering, Ocean University of China, Qingdao 266100, China – sequence: 2 givenname: Atilla surname: Incecik fullname: Incecik, Atilla organization: Department of Naval Architecture, Ocean, and Marine Engineering, University of Strathclyde, Glasgow G11XQ, United Kingdom – sequence: 3 givenname: Shizhe surname: Feng fullname: Feng, Shizhe organization: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China – sequence: 4 givenname: M.K. surname: Gupta fullname: Gupta, M.K. organization: Faculty of Mechanical Engineering, Opole University of Technology, Opole, 45758, Poland – sequence: 5 givenname: Grzegorz surname: Królczyk fullname: Królczyk, Grzegorz organization: Faculty of Mechanical Engineering, Opole University of Technology, Opole, 45758, Poland – sequence: 6 givenname: Z orcidid: 0000-0002-7265-0008 surname: Li fullname: Li, Z email: z.li@po.edu.pl organization: Donghai Laboratory, Zhoushan 316021, China |
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