A sparse auto-encoder-based deep neural network approach for induction motor faults classification-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

A sparse auto-encoder-based deep neural network approach for induction motor faults classification

Published:2016-07 Issue: Volume:89 Page:171-178
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Sun Wenjun,Shao Siyu,Zhao Rui,Yan Ruqiang,Zhang Xingwu,Chen Xuefeng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference36 articles.

1. A review of induction motors signature analysis as a medium for faults detection;Benbouzid;IEEE Trans. Ind. Electron.,2000

2. Intelligent fault monitoring and diagnosis in electrical machines;Huang;Measurement,2013

3. AI techniques in induction machines diagnosis including the speed ripple effect;Filippetti;IEEE Trans. Ind. Appl.,1998

4. Signatures of electrical faults in the force distribution and vibration pattern of induction motors;Rodrigue;IEE Proc. – Electr. Power Appl.,2006

5. Motor square current signature analysis for induction motor rotor diagnosis;Fernão Pires;Measurement,2013

Cited by 557 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. Integrating self-attention mechanisms in deep learning: A novel dual-head ensemble transformer with its application to bearing fault diagnosis;Signal Processing;2025-02

2. Diagnosis of electrical submersible pump failure using deep learning model with sand-water flow experimental data;Geoenergy Science and Engineering;2024-12

3. A gear fault diagnosis method based on reactive power and semi-supervised learning;Measurement Science and Technology;2024-09-09

4. Enhanced fault feature extraction and bearing fault diagnosis using shearlet transform and deep learning;Signal, Image and Video Processing;2024-09-06

5. Deep image clustering: A survey;Neurocomputing;2024-09

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