摘要
作为捕获风能的关键部件,风机叶片受制造及运行载荷影响,可能存在不同程度的损伤,这会直接影响风机运行可靠性。为防止风机叶片持续损伤发生质量安全事故,需要开发1种快速简便且非植入安装式的检测方法来识别风机叶片的损伤情况。根据叶片损伤和叶片运行噪声间存在的物理相关性,提出了一种基于声学信号和卷积神经网络(convolutional neural network,CNN)的风机叶片损伤检测方法,将时序声学信号转换成二维频谱图片,结合健康频谱图生成残差频谱图,并通过训练卷积神经网络来识别风机叶片是否存在损伤。分析结果表明:该算法消除了叶轮旋转产生的固有叶片扫风声音对损伤识别的影响,提高了识别精度;以某地风机的实测数据为例进行算法分析,结果表明该算法的分类精度达到了96.9%,验证了基于卷积神经网络的检测方法的有效性和精确性。
As a critical component for capturing wind energy, wind turbine blades may subject different degrees of damage due to blade manufacturing and operating load, which directly affects the reliability of wind turbine operation. For preventing quality and safety accidents, a fast and easy non-implantable detection method is needed to identify the damages. According to the physical correlation between blade damage and blade operation noise, a blade damage detection method based on acoustic signal and convolutional neural network(CNN) is proposed. The method converts the time-series acoustic signal into a two-dimensional spectral picture and combines the healthy spectral picture to generate a residual spectral picture. Then, the residual spectrogram is used to train the convolutional neural network and detect the damage. The analysis results show that the algorithm eliminates the influence of the inherent blade sweeping sound generated by the impeller rotation on the damage identification and improves the identification accuracy. The algorithm analysis was carried out with the actual measured data of a local wind turbine, and the results showed that the classification accuracy of the algorithm reached 96.9%, which verified the effectiveness and accuracy of the detection method based on convolutional neural network.
作者
刘启栋
LIU Qidong(Qinghai Huanghe Wind Power Generation Co.,Ltd.,Hainan 813000,China)
出处
《热力发电》
CAS
CSCD
北大核心
2023年第3期88-93,共6页
Thermal Power Generation
关键词
风机叶片
损伤检测
声信号
卷积神经网络
wind turbine blade
damage detection
acoustic signal
convolutional neural network
