针对接触式测量方法易受传感器采集通道限制与附加质量的问题,提出了一种基于三维振动信息融合的卷积神经网络风力机叶片裂纹诊断方法。首先,在双目摄影测量原理的基础上,提出一种三维振动信息融合的多通道样本构造方法,该方法可以集成叶片表面空间分布的多测点运动信息,得到更丰富的信号,且能极大地减少附加质量的干扰。其次,为了获取裂纹多层次的语义信息,提出一种新的多尺度卷积神经网络。选用某型1.5 kW的风力机叶片开展裂纹诊断实验,建立不同裂纹状态样本数据库,预测精度达到了93.4%,验证了所提方法的有效性。通过与经典的LeNet-5和VGG-11网络对比分析表明,改进的卷积神经网络具有更高的识别精度和更快的收敛速度,多通道样本在风力机叶片裂纹故障诊断中具有较好的应用效果。

To solve the problem that the contact measurement method is susceptible to the limitation of the sensor acquisition channel and the additional weight, a wind turbine blade crack diagnosis method based on three-dimensional (3D) vibration information fusion and convolutional neural network is proposed. First, based on the principle of binocular photogrammetry, a multi-channel sample construction method of 3D vibration information fusion is proposed. This method can integrate the motion information of multiple measurement points on the surface of the wind turbine blade, gain the acquired signal with more abundant features, and greatly decrease additional weight interference. Secondly, in order to obtain multi-level semantic information of cracks, a new multi-scale convolutional neural network is proposed. A type of 1.5 kW wind turbine blade was selected to carry out crack diagnosis experiments, and a database of samples of different crack states was established. The prediction accuracy reached 93.4%, which verified the effectiveness of the proposed method. Comparative analysis with the classic LeNet-5 and VGG-11 networks shows that the improved convolutional neural network has higher identify precision and faster convergence speed. Multi-channel signal samples can offer a better effect in wind turbine blade crack fault diagnosis application.