浮式风机叶片异常声压预测数据

基于采集的频率（Hz）、距离(m)、幅值(m)、介质密度(kg/m2)及声速(m/s)等关键参数，可精确计算风机叶片在运行过程中的气动噪声声压值。通过对比预设的气动噪声阈值区间，能够有效识别叶片是否存在异常噪声（如湍流分离、翼型失速或结构损伤）。该技术可实时预警潜在故障，显著提升风电机组运行安全性，并为叶片维护策略提供数据支撑，从而保障风电场的稳定高效发电。1.数据搜集：搜集到浮式风机叶片运行的声学振动信号数据，并将其作为特征变量。同时，收集对应的声压数据，作为目标变量。2.预处理：利用归一化公式x=（xi-min）/（max-min）；其中xi是样本字段中第i条数据(xi代表频率 (Hz)、距离 (m)、幅值 (m)、介质密度 (kg/m2)、声速 (m/s))，x是归一化后的值；3.模型训练：用Python语言环境运行，方法为XGBoost算法，预测浮式风机叶片的声压。XGBoost的目标函数由两部分组成：损失函数和正则化项。损失函数用于衡量模型预测值与实际值之间的差异，而正则化项则用于控制模型的复杂度，防止过拟合。公式如下：F(x）=求和L（yi，y^i）+求和（m(f(k))），其中L（yi，y^i）表示第i个样本的损失函数，yi是实际值，y^i是预测值；m(f(k))表示第 k 棵树的复杂度。当损失函数F（x）最小化时则停止迭代过程，在此过程中y^i预测值是通过特征函数进行求解的，具体计算方式如下：通过对于目标函数的求导，紧接着并令导数等于0，从而解出叶子节点的最优分数。通过不断地选择最优分裂点并构建树结构，XGBoost最终可以得到一棵最优的树模型。然后，将多棵树模型的预测结果进行累加，即可得到最终的预测值。4.模型迭代和更新，利用交叉验证来评估模型的稳定性和性能，其中交叉验证公式如下：m=求和（yi-y^i）/N；其中m代表交叉验证误差、N代表数据样本的数量、yi代表第 i 个样本的实际观测值、y^i代表第 i 个样本的模型预测值。若满足交叉验证的误差范围则退出迭代过程，输出模型参数值。5.结果分析，当声压值的绝对值在[0,25]之间时，判定为声压稳定，当大于25时候，判定为声压异常。

Based on key collected parameters including frequency (Hz), distance (m), amplitude (m), medium density (kg/m²) and sound velocity (m/s), the sound pressure value of aerodynamic noise generated during the operation of wind turbine blades can be accurately calculated. By comparing with the preset aerodynamic noise threshold range, abnormal noises (such as turbulent separation, airfoil stall or structural damage) on the blades can be effectively identified. This technology can provide real-time early warning for potential faults, significantly improve the operational safety of wind turbines, and provide data support for blade maintenance strategies, thereby ensuring the stable and efficient power generation of wind farms.
1. Data Collection: Collected acoustic vibration signal data from the operation of floating wind turbine blades, which are used as feature variables. Meanwhile, corresponding sound pressure data was collected as target variables.
2. Preprocessing: The normalization formula $x = frac{x_i - ext{min}}{ ext{max} - ext{min}}$ is adopted, where $x_i$ refers to the i-th data in the sample field (including frequency (Hz), distance (m), amplitude (m), medium density (kg/m²) and sound velocity (m/s)), and $x$ is the normalized value.
3. Model Training: Implemented in the Python environment, the XGBoost algorithm is used to predict the sound pressure of floating wind turbine blades. The objective function of XGBoost consists of two parts: the loss function and the regularization term. The loss function measures the difference between the model's predicted values and actual values, while the regularization term controls the model's complexity to prevent overfitting. The formula is as follows: $F(x) = sum_{i=1}^N L(y_i, hat{y}_i) + sum_{k=1}^K m(f^{(k)})$, where $L(y_i, hat{y}_i)$ represents the loss function of the i-th sample, $y_i$ is the actual value, and $hat{y}_i$ is the predicted value; $m(f^{(k)})$ represents the complexity of the k-th tree. The iteration stops when the objective function $F(x)$ is minimized. During this process, the predicted value $hat{y}_i$ is solved via the feature function. The specific calculation steps are as follows: take the derivative of the objective function, then set the derivative to 0 to solve for the optimal score of each leaf node. By continuously selecting optimal split points and constructing tree structures, XGBoost finally obtains an optimal tree model. Then, the final predicted value is obtained by accumulating the prediction results of multiple tree models.
4. Model Iteration and Update: Cross-validation is used to evaluate the stability and performance of the model, with the cross-validation formula as follows: $m = frac{sum_{i=1}^N (y_i - hat{y}_i)}{N}$, where $m$ represents the cross-validation error, $N$ represents the number of data samples, $y_i$ represents the actual observed value of the i-th sample, and $hat{y}_i$ represents the model's predicted value of the i-th sample. The iteration process exits when the cross-validation error meets the preset range, and the model parameters are output.
5. Result Analysis: When the absolute value of the sound pressure value is within [0, 25], it is determined that the sound pressure is stable; when it is greater than 25, it is determined that the sound pressure is abnormal.

搜集汇总

背景与挑战

背景概述

该数据集名为'浮式风机叶片异常声压预测数据'，主要用于训练和测试机器学习模型，以预测浮式风机叶片的异常声压。数据以CSV格式存储，包含1000条记录，关键词涵盖浮式风机、叶片异常和声压预测，适用于相关领域的研究和应用。