A Feature Selection Approach Based on Memory Space Computation Genetic Algorithm Applied in Bearing Fault Diagnosis Model
Author:
Affiliation:
1. Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan
2. Department of Electrical and Electronic Engineering, Thu Dau Mot University, Thu Dau Mot, Binh Duong, Vietnam
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10121764.pdf?arnumber=10121764
Reference48 articles.
1. Bearing Performance Degradation Assessment Based on Ensemble Empirical Mode Decomposition and Affinity Propagation Clustering
2. Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment
3. Intelligence Bearing Fault Diagnosis Model Using Multiple Feature Extraction and Binary Particle Swarm Optimization With Extended Memory
4. Very High Resolution Remote Sensing Imagery Classification Using a Fusion of Random Forest and Deep Learning Technique—Subtropical Area for Example
5. A Novel Intelligent Fault Diagnosis Method Based on Variational Mode Decomposition and Ensemble Deep Belief Network
Cited by 5 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. Application of symmetric uncertainty and emperor penguin — Grey wolf optimisation for feature selection in motor fault classification;IET Electric Power Applications;2024-08-07
2. GABoT: A Lightweight Real-Time Adaptable Approach for Intelligent Fault Diagnosis of Rotating Machinery;Journal of Vibration Engineering & Technologies;2024-06-07
3. Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis;Mathematics;2024-05-31
4. Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis;IEEE Open Journal of the Industrial Electronics Society;2024
5. Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms;IEEE Access;2023
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