基于改进LeNet-5优化算法的轴承故障诊断研究

Bearing Fault Diagnosis Based on the Improved LeNet-5 Optimization Algorithm

针对直升机自动倾斜器滚动轴承振动信号复杂而传统卷积神经网络对轴承故障信号微小特征提取困难导致的故障诊断精度不高的问题,提出基于LeNet-5网络的一种改进方法。首先,在LeNet-5网络中设计一个新的特征提取模块,形成并行的特征提取框架,增强网络对微小特征的提取能力,缓解直升机故障诊断精度不高的问题。其次,采用Dropout层和自适应的参数算法,避免模型不稳定,加速模型的收敛。最后,利用课题组轴承数据和西储大学公开数据集开展实验,结果表明,相较于原LeNet-5网络模型,改进后的LeNet-5网络具有较高的测试精度,在课题组数据集的测试精度达99.6%,西储大学数据集的测试精度为100%,说明该模型对滚动轴承的故障诊断具有更高的准确率。

In response to the problem of low accuracy of fault diagnosis caused by the complexity of rolling bearing vibration signals of helicopter auto-tilter and the difficulty of extracting tiny features of bearing fault signals by traditional convolutional neural network,an improved method of LeNet-5 network is proposed.First,a new feature extraction layer is added to the LeNet-5 network and a parallel feature extraction framework is formed,aiming to enhance the extracting minute feature’s ability of network and alleviate the problem of low accuracy of helicopter fault diagnosis.Moreover,to improve the stability of the model and accelerate its convergence,a dropout layer and an adaptive parameter algorithm are adopted.Finally,experiments are carried out with the dataset of research group and the public dataset of Western Reserve University.The experiment results demonstrate that the improved LeNet-5 network has a higher test accuracy compared with the original LeNet-5 network model,with 99.6%in the research group dataset and 100%in Western Reserve University dataset,which verifies that the model has a higher accuracy rate in the fault diagnosis of rolling bearings.