数据驱动振动因素对海上风机设备腐蚀影响

Influence of Vibration Factors on Corrosion of Offshore Wind Turbine Equipment by Data Driven

基于腐蚀大数据技术,旨在探究风机震动以及海洋环境因素对风电风机腐蚀行为的影响,并提出一种可靠的腐蚀电流预测模型。将海上风电风机的震动数据,环境数据以及腐蚀电流数据作为输入特征,利用线性回归,支持向量机,随机森林和梯度回归四种机器学习方法,对腐蚀电流进行深预测。结果显示,随机森林模型因其出色的非线性拟合能力在所有模型中表现最佳。此外,还发现温度和湿度是影响腐蚀电流的关键环境因素,而振动数据的影响相对较小。揭示了各种因素对海上风电风机腐蚀的影响,建立了一种可靠的机器学习模型来预测风机腐蚀行为。为风机的材料选择以及腐蚀防护措施的优化提供了科学依据。

Based on big data technology in corrosion,aiming to explore the effects of wind turbine vibration and marine environmental factors on the corrosion behavior of wind turbines for wind power,and proposes a reliable corrosion current prediction model.The vibration data of offshore wind turbines,environmental data,and corrosion current data are used as input features.Linear regression,support vector machine,random forest,and gradient boosting regression are employed as machine learning methods for deep prediction of corrosion current.The results show that the random forest model performs best among all models due to its excellent non-linear fitting ability.Furthermore,temperature and humidity are found to be key environmental factors affecting corrosion current,while the impact of vibration data is relatively minor.The study reveals the influence of various factors on the corrosion of offshore wind turbines and establishes a reliable machine learning model for predicting wind turbine corrosion behavior.It provides a scientific basis for the material selection and optimization of corrosion protection measures for wind turbines.