基于多尺度卷积自编码器和生成对抗网络的轴承故障诊断深度特征提取方法,Measurement Science and Technology

当前位置： X-MOL 学术 › Meas. Sci. Technol. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

基于多尺度卷积自编码器和生成对抗网络的轴承故障诊断深度特征提取方法
Measurement Science and Technology ( IF 2.7 ) Pub Date : 2022-03-09 , DOI: 10.1088/1361-6501/ac56f0
Zhiyong Hu ₁ , Taorui Han ₁ , Jun Bian ₂ , Ziwei Wang ₁ , Liu Cheng ₁ , Wenlei Zhang ₁ , Xiangwei Kong _{1,

3}

Affiliation

轴承的振动信号与其故障密切相关。从信号中提取的特征的好坏对故障诊断的准确性有很大的影响。在本文中，提出了一种结合多尺度自动编码器（AE）和生成对抗网络的新方法来提取信号的深度敏感特征，并结合分类器进行故障诊断。AE作为生成器（即生成器由encoder和decoder组成），采用对抗和重构的思想进行训练。生成器训练得越好，编码器训练得越好，也就是说编码器提取的特征（编码器的输出）越好。然后将这些特征作为新的输入，送入分类器进行分类，最终得到故障类型。该方法解决了传统轴承故障诊断方法代表性弱、过度依赖专业知识的问题。同时，与大多数现有的故障诊断神经网络模型相比，它具有更高的准确性，尤其是在困难的诊断任务中。为进一步验证所提模型的有效性，搭建了轴承试验台，并将采集到的数据用于故障诊断，证明了所提方法的优越性。

"点击查看英文标题和摘要"

A deep feature extraction approach for bearing fault diagnosis based on multi-scale convolutional autoencoder and generative adversarial networks

The vibration signal of a bearing is closely related to its fault. The quality of the features extracted from the signal has a great impact on the accuracy of fault diagnosis. In this paper, a new method combining multi-scale autoencoder (AE) and generative adversarial network is proposed to extract the depth-sensitive features of the signal, and unite with the classifier for fault diagnosis. The AE is used as the generator (i.e. the generator is composed of encoder and decoder), and the idea of confrontation and reconstruction is used for training. The better the training of the generator, the better the training of the encoder, which means that the extracted feature of the encoder (the output of the encoder) is better. Then take these features as new inputs, send them to the classifier for classification, and finally get the fault type. This method solves the problems of weak representation and over-reliance on professional knowledge of the traditional method for bearing fault diagnosis. Meanwhile, compared with most existing neural network models for fault diagnosis, it has higher accuracy, especially in difficult diagnosis tasks. To further verify the effectiveness of the proposed model, a bearing test rig is established, and the collected data are used for fault diagnosis to prove the superiority of the proposed method.

更新日期：2022-03-09

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南

全部期刊列表>>