A time-saving fault diagnosis using simplified fast GAN and triple-type data transfer learning

Author:

Zhong Hongyu12ORCID,Yu Samson2,Trinh Hieu2,Yuan Rui1ORCID,Lv Yong1ORCID,Wang Yanan2

Affiliation:

1. Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China

2. Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

Abstract

Existing intelligent fault diagnosis approaches demand substantial data for training diagnostic models. However, factors such as the inherent characteristics of bearings, operating conditions, and privacy security make collecting comprehensive fault-bearing data very difficult. Although generating synthetic data through generative adversarial networks (GANs) is feasible, the data generation of GANs is a time-consuming process. To address these challenges, a fault diagnosis framework based on GAN and deep transfer learning (DTL) is proposed, termed the simplified fast GAN triple-type data transfer learning (SFGAN-TDTL) method. Initially, an SFGAN is proposed as a replacement for traditional GANs. The time-frequency image data generated by SFGAN serve to augment the training dataset, offering faster and higher-quality data generation compared to traditional GANs. To further reduce time consumption for GAN-based methods, the TDTL method is proposed. Differing from DTL, which utilizes synthetic data to construct a pre-trained model and conducts targeted fine-tuning with real data, TDTL employs open-source data, synthetic data, and real data to fill the weights of the task-insensitive layer, task-sensitive layer, and fully connected layer, respectively. Numerical results demonstrate that SFGAN-TDTL maintains higher diagnostic accuracy while significantly reducing time consumption.

Funder

Hubei Natural Science Foundation Innovation Development Joint Key Program

Hubei Natural Science Foundation Youth Program

National Natural Science Foundation of China

Natural Science Foundation Innovation Group Program of Hubei Province

Publisher

SAGE Publications

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3