A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach

Mousavi，Zohreh1,2; Varahram，Sina1; Ettefagh，Mir Mohammad1; Sadeghi，Morteza H.1; Feng，Wei Qiang2,3; Bayat，Meysam2,4

2024-01-15

10.1016/j.oceaneng.2023.116563

Ocean Engineering   影响因子和分区

0029-8018

Engineering has many necessary fields, and Structural Health Monitoring (SHM) is one of the most important of them. Sometimes in industrial environments, it is difficult and even impossible to collect data containing different real damages. Therefore, the problem of data acquisition represents a primary challenge in designing damage detection systems. The application of digital twin methods based on simulated models and/or Machine Learning (ML) models is a practical way to solve this problem. In this approach, a digital twin is generated for a compromised structure, utilizing a physics-based model to analyze diverse damage scenarios. Subsequently, an ML model is trained using data extracted from the physics-based model, functioning as the digital twin. This research proposes a method based on a digital twin for detecting damages in structures. The data produced from a Floating Wind Turbine (FWT) model was used to evaluate the performance of the proposed digital twin-based method. For this purpose, the FWT structure was simulated using a numerical model to address the data collection problem in the face of various uncertainties, such as changing loading conditions. In line with the concept of digital twin and to reduce the computational time, a Deep Convolution Long Short-Term Memory Neural Network (DCLSTMNN) model was designed and trained only with the frequency data of various scenarios of the simulated FWT model under constant loads (deterministic loads, including constant wind speed and airy wave model) to learn the damage-sensitive features. Then, to demonstrate the robustness of the proposed model under different uncertainties, the DCLSTMNN model was evaluated using the frequency data of the simulated FWT model under variable loading conditions (including Kaimal wind model and JONSWAP wave theory). Some vibration response components unrelated to the nature of the FWT model were removed using the Complete Ensemble Empirical Mode Decomposition (CEEMD) method. Then, the reconstructed vibration responses were used to create the frequency data using the Frequency Domain Decomposition (FDD) technique. The study results show that the proposed digital twin-based method can detect the location and severity of damage more accurately than other comparable methods despite various uncertainties.

Deep learning Digital twin Floating wind turbine structure Structural health monitoring Uncertainties Various loading conditions

EI

英语

其他

ENGINEERING

2-s2.0-85180578697

Scopus

1.Department of Mechanical Engineering,University of Tabriz,Tabriz,Iran
2.Department of Ocean Science and Engineering,Southern University of Science and Technology,Shenzhen,China
3.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou),Guangzhou,China
4.Department of Civil Engineering,Najafabad Branch,Islamic Azad University,Najafabad,Iran

Mousavi，Zohreh,Varahram，Sina,Ettefagh，Mir Mohammad,et al. A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach[J]. Ocean Engineering,2024,292.

Mousavi，Zohreh,Varahram，Sina,Ettefagh，Mir Mohammad,Sadeghi，Morteza H.,Feng，Wei Qiang,&Bayat，Meysam.(2024).A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach.Ocean Engineering,292.

Mousavi，Zohreh,et al."A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach".Ocean Engineering 292(2024).