Support vector regression-based fatigue damage assessment method for wind turbine nacelle chassis

Liu, Yongqian; Tao, Tao; Zhao, Xingyu; Zhang, Ce; Ma, Yan

doi:10.1016/j.istruc.2021.04.093

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…Comprehensive evaluation of the IFs of fatigue damage. [127] Table 6. Prediction methods of fatigue strength.…”

Section: Characteristics Prediction Of Fatigue Crackmentioning

confidence: 99%

“…[90] As shown in Figure 15, a database containing equivalent stress amplitude through precalculation was established to comprehensively evaluate the IFs of fatigue performance via support vector machine (SVM). [127] The dispersion evaluation method for fatigue life can be conducted to analyze the IFs related to fatigue life and comprehensively evaluate the importance of the IFs. [128,129] Moreover, a reliable ANN model was established via the full consideration of the effects of loading rate, plate thickness, nugget size, joint type, and material type on fatigue strength.…”

Section: Characteristics Referencesmentioning

confidence: 99%

See 1 more Smart Citation

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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Fatigue fracture of welded joints is an important cause for engineering accidents. Due to the coexistence of so many influencing factors, the current prediction model of fatigue behavior cannot be used for all service conditions. The progress of each module (database establishment, data processing, data augmentation, dimension reduction, and comprehensive consideration of influencing factors) during the process of constructing a prediction model is summarized herein. Specifically, the state‐of‐art prediction models of fatigue strength, fatigue crack growth rate, fatigue life, and fatigue reliability are introduced. Moreover, suggestions for future work are given at the end of each section. Finally, according to the influential prediction methods, the establishment of fatigue performance prediction methods of welded joints via data‐driven method is summarized.

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“…Comprehensive evaluation of the IFs of fatigue damage. [127] Table 6. Prediction methods of fatigue strength.…”

Section: Characteristics Prediction Of Fatigue Crackmentioning

confidence: 99%

Section: Characteristics Referencesmentioning

confidence: 99%

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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show abstract

“…Comparing the performance of the two algorithms, the authors determine that the ANN is more accurate. [12] offer a hybrid machine learning model that integrates wavelet transform, PCA, and ANN for wind speed forecasting. In terms of accuracy, the suggested model surpasses other machine learning techniques and conventional forecasting techniques.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Novel Linear Regression Algorithm with Improved Accuracy Compared over K-Nearest Neighbor in Predicting Wind Power Generation

Deepak,

Jagadeesh

2024

E3S Web Conf.

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This work proposes a novel Linear Regression algorithm compared its performance with the K-Nearest Neighbor (KNN) algorithm for improving the accuracy of wind power generation prediction.In this study, two groups were created for the purpose of comparing the effectiveness of the KNN model (group 1) and the Linear Regression model (group 2) in predicting wind energy output. Each group consisted of 10 samples, resulting in a total of 20 samples used for the analysis. The data in this study were collected from an actual wind turbine and include the following factors: wind speed, altitude, humidity, air density, wind direction, and output power. The information was gathered at 10-minute intervals over the course of a year. The dataset was preprocessed, and the mean value of the corresponding variable was used to impute the missing values. Seventy percent of the data was used for training and thirty percent for testing. The training set was used to train the models, whilst the testing set was used to assess the effectiveness of the models. Python’s scikit-learn module was made use for the development of the Linear Regression technique. Based on statistical power (G-power) = 0.8, α = 0.05, CI of 95% confidence interval was also calculated. The observations indicate that the Linear Regression algorithm is more accurate than the KNN technique. The linear regression model achieved an accuracy of 82.15%, whereas the KNN model had a lower accuracy of 79.55% for predicting wind energy output. Additionally, the statistically significance values of the research was determined to be at a p-value of 0.001 (p<0.05). The algorithm was implemented and evaluated using real-world wind power generation data, and the findings demonstrate that, in terms of accuracy, This Linear Regression algorithm surpasses the KNN approach.

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“…As one of the most commercially promising and dynamic renewable energy sources, wind energy has many advantages, such as a large space for the growth of installed units, a safe way of obtaining energy, and inexhaustibility [1]. As a typical large mechanical piece of equipment, the wind turbine usually operates under alternating loads and harsh natural environments, such that its components are liable to fatigue failure during their 20-year lifetime [2]. As the core component of wind turbines, the performance of main shaft bearings directly affects the transmission efficiency and reliability of wind turbine systems.…”

Section: Introductionmentioning

confidence: 99%

Reliability Sensitivity Analysis of Main Shaft Bearings of Wind Turbines Subject to Subsurface Stress

et al. 2023

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As the core component of a wind turbine, the performance of main shaft bearings directly affects the transmission efficiency and reliability of wind turbines. To the best of our knowledge, few reliability analyses of wind power bearings have been carried out with the consideration of mechanical properties. In this paper, a finite element model is established to calculate the subsurface stress of the main shaft bearing of a wind turbine, considering the structural thicknesses, the friction conditions, and the interference conditions of the bearing. The randomness of several factors is considered, including structural thicknesses, material parameters, friction coefficients and the interference of the bearing. Latin hypercube design is used to get sample points, and the bearing’s mechanical responses of these sample points are analyzed. Through the data of these sample points, a Kriging model is established. The comparison with the finite element results shows that the Kriging model greatly improves the computational efficiency of the finite element model, with a relative error result of only 3.80 × 10−5. The Monte Carlo simulation method is applied to analyze the reliability and sensitivity of the bearing’s subsurface stress. The results show that an increase in the inner ring thickness will improve the bearing’s stress reliability, while an increase in other parameters will reduce the bearing’s stress reliability, including outer ring thickness, roller length, material elastic modulus, density, bearing and stationary shaft interference, and friction coefficients. The research results provide a reasonable reference for optimizing the design of the structure, assembly and material selection for main shaft bearings of wind turbines.

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Support vector regression-based fatigue damage assessment method for wind turbine nacelle chassis

Cited by 5 publications

References 24 publications

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Performance Analysis of Novel Linear Regression Algorithm with Improved Accuracy Compared over K-Nearest Neighbor in Predicting Wind Power Generation

Reliability Sensitivity Analysis of Main Shaft Bearings of Wind Turbines Subject to Subsurface Stress

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