摘要
轴承通常工作于复杂噪声环境下,使得时域振动信号容易受到各种噪声的污染,从而误导诊断结果。针对以上问题,提出基于一维卷积自编码(1D-DCAE)和一维卷积神经网络(1D-CNN)的联合抗噪故障诊断算法。为了模拟真实噪声环境,在原始振动信号中添加不同信噪比的高斯噪声,用1D-DCAE对原始信号降噪,再将降噪信号用于1D-CNN进行故障诊断。基于全卷积神经网络搭建1D-DCAE模型,并舍弃池化层以降低信息丢失,以提高联合诊断模型的抗噪能力。结果表明:采用基于全卷积网络搭建的1D-DACE有更好的降噪效果,改进后的模型能自适应诊断各种噪声环境下的故障。
Bearings usually work in the complex noise environment,which makes the time-domain vibration signal easy to be polluted by various noises,thus misleading the diagnosis results.To solve this problem,a method combining a one-dimensional(1-D)denoising convolutional autoencoder(DCAE)and a(1-D)convolutional neural network(CNN)is proposed.In order to simulate the real noise environment,Gaussian noise with different signal-to-noise ratio was added to the original vibration signal.1D-DCAE was used to denoise the original signals,and then the denoised signal was used for 1D-CNN fault dignosis.1D-DCAE was built based on full convolution network(FCN)and the pooling layer was discarded to improve the anti-noise capability.The results show that FCN-based 1D-DCAE has better noise reduction effect,and the improved model can adaptively diagnose faults of various noise environments.
作者
刘冲
Liu Chong(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China)
出处
《华东交通大学学报》
2020年第4期82-87,共6页
Journal of East China Jiaotong University
关键词
降噪自编码
卷积神经网络
故障诊断
抗噪诊断
denoising convolutional autoencoder(DCAE)
convolutional neural network(CNN)
fault diagnosis
anti-noise diagnosis
