Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-022-01757-4.pdf
Reference83 articles.
1. Abid A, Khan MT, Khan MS (2017) Multidomain features-based GA optimized artificial immune system for bearing fault detection. IEEE Trans Syst Man Cybern Syst 50:348–359
2. Afia A, Rahmoune C, Benazzouz D, Merainani B, Fedala S (2020) New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network. Adv Mech Eng 12:1687814020916593
3. Amarnath M, Sugumaran V, Kumar H (2013) Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement 46:1250–1256
4. Anagnostopoulos C, Tasoulis DK, Adams NM, Pavlidis NG, Hand D (2012) Online linear and quadratic discriminant analysis with adaptive forgetting for streaming classification. Stat Anal Data Min ASA Data Sci J 5:139–166
5. Bessam B, Menacer A, Boumehraz M, Cherif H (2017) Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor. Int J Syst Assur Eng Manag 8:478–488
Cited by 6 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 multi-fault diagnosis method for rolling bearings;Signal, Image and Video Processing;2024-08-13
2. Exploring Sound Versus Vibration for Robust Fault Detection on Rotating Machinery;IEEE Sensors Journal;2024-07-15
3. Graph neural network-based bearing fault diagnosis using Granger causality test;Expert Systems with Applications;2024-05
4. Systematic Review on Fault Diagnosis on Rolling-Element Bearing;Journal of Vibration Engineering & Technologies;2024-04-10
5. Neural Network and L-kurtosis for Diagnosing Rolling Element Bearing Faults;Journal of Electrical Engineering & Technology;2024-03-19
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