A novel deep clustering network using multi-representation autoencoder and adversarial learning for large cross-domain fault diagnosis of rolling bearings

Haoran Wen; Wei Guo; Xiang Li

首页> 外文期刊>Expert Systems with Application >A novel deep clustering network using multi-representation autoencoder and adversarial learning for large cross-domain fault diagnosis of rolling bearings

【24h】

A novel deep clustering network using multi-representation autoencoder and adversarial learning for large cross-domain fault diagnosis of rolling bearings

机译：A novel deep clustering network using multi-representation autoencoder and adversarial learning for large cross-domain fault diagnosis of rolling bearings

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

Intelligent fault diagnosis based on deep learning has been more attractive in practical engineering. However, its accuracy is constrained by unlabeled data and large domain shift for cross-domain fault diagnosis. As an un-supervised learning network, the autoencoder (AE) can relieve the pressure of unlabeled data. Using it as a building block, this paper presents a novel deep clustering network, named as clustering graph convolutional network with multiple adversarial learning (c-GCN-MAL) for intelligent fault diagnosis of various bearings. First, multiple representation of datasets, i.e., the datasets and their structured relatedness, are extracted by AE and graph convolutional network (GCN) to recognize different classes and their related information. Then, the data-adversarial and domain-adversarial are individually set by newly defining the loss function of the proposed deep network, so that its clustering and transfer ability for new dataset can be enhanced. In experiments, it is applied to four open-source bearing datasets and one dataset of practical bearings. Transfer tasks between different rotation speeds, damage generation, and experimental bearings, even the transfer from experimental bearings to wheelset bearings used in real tests, are assigned to verify the effectiveness of the proposed network. The experimental results and comparisons indicate that the proposed deep clustering network is feasible for the transfer with large domain shift and can provide noticeably higher accuracy and stable results for cross-domain fault diagnosis of bearings.

著录项

来源
《Expert Systems with Application》 |2023年第9期|120066.1-120066.14|共14页
作者
Haoran Wen; Wei Guo; Xiang Li;
展开▼
作者单位

School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China;

School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类
关键词
Data clustering; Autoencoder; Deep learning; Transfer learning; Fault diagnosis; Bearing;

A novel deep clustering network using multi-representation autoencoder and adversarial learning for large cross-domain fault diagnosis of rolling bearings

摘要

著录项

相关主题

期刊订阅