基于改进卷积神经网络的风电轴承故障诊断策略

针对风电机组滚动轴承故障特征微弱、提取困难、诊断效率低下等问题,提出一种基于改进卷积神经网络(Convolution Neural Network,CNN)的故障诊断算法。改进CNN模型结构,在全连接层前增加新的卷积层,挖掘信号的深层特征以提高模型的泛化能力。对卷积层数据进行批归一化处理,采用带有动量的随机梯度下降训练算法来加速训练速度。详细介绍了改进CNN的工作原理,给出了采用改进CNN进行故障诊断的流程。最后利用凯斯西储大学滚动轴承数据库的数据进行验证。证明该方法不需要预先提取信号的故障特征,可直接实现对轴承的故障特征提取以及故障识别,诊断率高。

Fault diagnosis strategy of a wind power bearing based on an improved convolutional neural network

The rolling bearing of a wind turbine has problems of weak fault characteristics, difficult extraction and low diagnosis efficiency. To solve these problems we propose a fault diagnosis algorithm based on an improved Convolution Neural Network (CNN). The structure of the CNN model is improved, a new convolution layer is added in front of the full connection layer, the deep features of the signal are excavated to improve the generalization ability of the model, the convolution layer data are standardized, and the stochastic gradient descent with momentum is used to speed up the training speed. The working principle of the improved CNN is introduced in detail, and the flow chart of fault diagnosis with improved CNN is given. Finally, the data of a rolling bearing database at Case Western Reserve University is used to verify the method, and proves that this method does not need to extract the fault features of the signal in advance, and can directly achieve fault feature extraction and fault identification of bearings, and the diagnosis rate is high. This work is supported by the National Natural Science Foundation of China (No. 61573167) and the Fundamental Research Funds for the Central Universities (No. JUSRP51510).