Sparse filtering based domain adaptation for mechanical fault diagnosis
Author:
Funder
National Natural Science Foundation of China
Publisher
Elsevier BV
Subject
Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications
Reference48 articles.
1. A survey of fault diagnosis and fault-tolerant techniques. Part II: fault diagnosis with knowledge-based and hybrid/active approaches;Gao;IEEE Trans. Ind. Electron.,2015
2. Deep quantum-inspired neural network with application to aircraft fuel system fault diagnosis;Gao;Neurocomputing,2017
3. A novel sparse filtering approach based on time-frequency feature extraction and softmax regression for intelligent fault diagnosis under different speeds;Zhang;J. Central South Univ.,2019
4. Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data;Jia;Mech. Syst. Signal Process.,2016
5. Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines;Wang;Neurocomputing,2019
Cited by 25 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A systematic literature review of deep learning for vibration-based fault diagnosis of critical rotating machinery: Limitations and challenges;Journal of Sound and Vibration;2024-11
2. Integrated decision-making with adaptive feature weighting adversarial network for multi-target domain compound fault diagnosis of machinery;Advanced Engineering Informatics;2024-10
3. Dual-weight attention-based multi-source multi-stage alignment domain adaptation for industrial fault diagnosis;Measurement Science and Technology;2024-06-05
4. A deep targeted transfer network with clustering pseudo-label learning for fault diagnosis across different Machines;Mechanical Systems and Signal Processing;2024-05
5. Towards multi-scene learning: A novel cross-domain adaptation model based on sparse filter for traction motor bearing fault diagnosis in high-speed EMU;Advanced Engineering Informatics;2024-04
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