基于EEMD的ICNN故障诊断方法

针对轴承微弱故障特征提取困难和故障诊断准确率低等问题,提出一种基于集合经验模态分解的改进卷积神经网络的故障诊断方法。首先,利用集合经验模态分解(EEMD)对信号进行降噪等预处理,并将预处理后的信号转换为二维信号;其次,为了解决数据特征不确定性和卷积神经网络(CNN)内部参数爆炸的问题,在CNN的卷积层和池化层之间增加批量归一化层进行标准化处理,得到改进的卷积神经网络(ICNN);最后,以风电机组轴承微弱故障数据集为例,验证了所提方法相较于其他诊断方法更具有优越性,能够有效提取故障特征,具有较高的准确率和诊断效率。

ICNN Fault Diagnosis Method Based on EEMD

An improved convolutional neural network fault diagnosis method based on ensemble empirical mode decomposition is proposed to extract weak fault features and low fault diagnosis accuracy of bearings. Firstly, we employ ensemble empirical mode decomposition to reduce the noise of the signal, and transform the denoised signal into a 2-dimensional (2-D) signal. Then, to solve the problem of parameter explosion of convolutional neural network (CNN) and the uncertainty of data characteristics, a batch normalization layer is added between the convolutional layer and the pooling layer of CNN to construct an improved convolutional neural network (ICNN). Finally, the weak fault dataset of the wind turbine bearings are taken as an example to verify that the proposed method has superior performance compared with other methods. The experimental results show that the proposed method can effectively extract fault features, and has high fault diagnosis accuracy and efficiency.