Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Feijóo, Maria del Cisne; Zambrano, Yovana; Vidal, Yolanda; Tutivén, Christian

doi:10.3390/s21103333

Cited by 23 publications

(15 citation statements)

References 31 publications

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“…Feijóo et al introduced an innovative autoencoder neural network model in their study [117]. Their methodology encompassed the following steps: simulating wind excitation with Gaussian white noise, collecting wind turbine data using accelerometers, preprocessing raw data (including cleaning, normalization, feature engineering, and addressing imbalanced data), and employing an autoencoder neural network for damage classification.…”

Section: Health Monitoring and Maintenancementioning

confidence: 99%

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Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Masoumi

2023

JMSE

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The continuous advancement within the offshore wind energy industry is propelled by the imperatives of renewable energy generation, climate change policies, and the zero-emission targets established by governments and communities. Increasing the dimensions of offshore wind turbines to augment energy production, enhancing the power generation efficiency of existing systems, mitigating the environmental impacts of these installations, venturing into deeper waters for turbine deployment in regions with optimal wind conditions, and the drive to develop floating offshore turbines stand out as significant challenges in the domains of development, installation, operation, and maintenance of these systems. This work specifically centers on providing a comprehensive review of the research undertaken to tackle several of these challenges using machine learning and artificial intelligence. These machine learning-based techniques have been effectively applied to structural health monitoring and maintenance, facilitating the more accurate identification of potential failures and enabling the implementation of precision maintenance strategies. Furthermore, machine learning has played a pivotal role in optimizing wind farm layouts, improving power production forecasting, and mitigating wake effects, thereby leading to heightened energy generation efficiency. Additionally, the integration of machine learning-driven control systems has showcased considerable potential for enhancing the operational strategies of offshore wind farms, thereby augmenting their overall performance and energy output. Climatic data prediction and environmental studies have also benefited from the predictive capabilities of machine learning, resulting in the optimization of power generation and the comprehensive assessment of environmental impacts. The scope of this review primarily includes published articles spanning from 2005 to March 2023.

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Section: Health Monitoring and Maintenancementioning

confidence: 99%

“…Feijóo et al [117] Autoencoders Damage classification in wind turbine structures using autoencoders.…”

Section: Technique Summarymentioning

confidence: 99%

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Masoumi

2023

JMSE

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“…Corrosion fatigue is the most common type of damage that affects offshore structures [34][35][36], with joints being the origin of such damage [37]. Therefore, the damage to the joints was addressed in this study.…”

Section: Damage Scenariosmentioning

confidence: 99%

Investigation on Applicability and Limitation of Cosine Similarity-Based Structural Condition Monitoring for Gageocho Offshore Structure

Kim

Oh²,

Min³

2022

Sensors

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The key to coping with global warming is reconstructing energy governance from carbon-based to sustainable resources. Offshore energy sources, such as offshore wind turbines, are promising alternatives. However, the abnormal climate is a potential threat to the safety of offshore structures because construction guidelines cannot embrace climate outliers. A cosine similarity-based maintenance strategy may be a possible solution for managing and mitigating these risks. However, a study reporting its application to an actual field structure has not yet been reported. Thus, as an initial study, this study investigated whether the technique is applicable or whether it has limitations in the real field using an actual example, the Gageocho Ocean Research Station. Consequently, it was found that damage can only be detected correctly if the damage states are very similar to the comparison target database. Therefore, the high accuracy of natural frequencies, including environmental effects, should be ensured. Specifically, damage scenarios must be carefully designed, and an alternative is to devise more efficient techniques that can compensate for the present procedure.

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“…This could train a certain network that has derivative functions for the weight, net-input and transfer functions. This algorithm can be summarized in five steps, by supposing that the beginning point for the local optimization operation is w(0) [51,52]:…”

Section: Scaled Conjugate Gradient Training Algorithmmentioning

confidence: 99%

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

2021

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In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is a monocrystalline cell with a rated production of 175 watts that is placed at Heliopolis University, Bilbéis city, Egypt. The NARX model is considered powerful enough to emulate the nonlinear dynamic state-space model. It is extensively performed to resolve a variety of problems and is mainly important in complex process control. Moreover, the NARX method is selected because of its quick learning and completion times, as well as high appropriateness, and is distinguished by advantageous dynamics and interference resistance. The neural network (NN) is trained and optimized with three algorithms, the Levenberg–Marquardt Algorithm (NARX-LMA), the Bayesian Regularization Algorithm (NARX-BRA) and the Scaled Conjugate Gradient Algorithm (NARX-SCGA), to attain the best performance. The forecasted results using the NARX method based on the three algorithms are compared with experimentally measured data. The NARX-LMA, NARX-BRA and NARX-SCGA models are validated using statistical criteria. In general, weather conditions have a significant impact on the execution and quality of the results.

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Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Cited by 23 publications

References 31 publications

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Investigation on Applicability and Limitation of Cosine Similarity-Based Structural Condition Monitoring for Gageocho Offshore Structure

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

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