摘要
Due to the fact that the vibration signal of the rotating machine is one-dimensional and the large-scale convolution kernel can obtain a better perception field, on the basis of the classical convolution neural network model(LetNet-5), one-dimensional large-kernel convolution neural network(1 DLCNN) is designed. Since the hyper-parameters of 1 DLCNN have a greater impact on network performance, the genetic algorithm(GA) is used to optimize the hyper-parameters, and the method of optimizing the parameters of 1 DLCNN by the genetic algorithm is named GA-1 DLCNN. The experimental results show that the optimal network model based on the GA-1 DLCNN method can achieve 99.9% fault diagnosis accuracy, which is much higher than those of other traditional fault diagnosis methods. In addition, the 1 DLCNN is compared with one-dimencional small-kernel convolution neural network(1 DSCNN) and the classical two-dimensional convolution neural network model. The input sample lengths are set to be 128, 256, 512, 1 024, and 2 048, respectively, and the final diagnostic accuracy results and the visual scatter plot show that the effect of 1 DLCNN is optimal.
由于旋转机械振动信号是一维的,且大尺寸卷积核可获得更大的感知野,在经典卷积神经网络模型(LetNet-5)的基础上设计了带有大尺寸卷积核的一维卷积核神经网络(1DLCNN).由于1DLCNN的超参数对网络性能影响较大,利用遗传算法对网络模型中的一些超参数进行寻优,并将这种利用遗传算法优化1DLCNN参数的方法称为GA-1DLCNN.试验结果表明,基于GA-1DLCNN方法所得到最优网络模型可以实现99. 9%的故障诊断精度,远远高于其他几种传统的故障诊断方法.此外,将采用大尺寸卷积核的一维卷积神经网络与小尺寸卷积核的一维卷积神经网络以及经典二维卷积神经网络模型进行对比,输入样本长度分别设定为128,256,512,1 024,2 048,最终的诊断精度结果以及可视化散点图显示1DLCNN的效果最优.
基金
The National Natural Science Foundation of China(No.51675098)
