Deep Learning Approach for Sensor Data Prediction and Sensor Fault Diagnosis in Wind Turbine Blade
Author:
Affiliation:
1. School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China
2. School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, China
Funder
National Natural Science Foundation of China
Basic and Applied Basic Research Foundation of Guangdong Province
Guangzhou Key Laboratory of Software-Defined Low Latency Network, China
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/ielx7/6287639/9668973/09938966.pdf?arnumber=9938966
Reference36 articles.
1. Fault Simulation and Online Diagnosis of Blade Damage of Large-Scale Wind Turbines
2. An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples
3. A New Hybrid Fault Detection Method for Wind Turbine Blades Using Recursive PCA and Wavelet-Based PDF
4. Fault diagnosis of wind turbine based on Long Short-term memory networks
5. Quantile regression neural network‐based fault detection scheme for wind turbines with application to monitoring a bearing
Cited by 8 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. Sensor Fault Detection and Classification Using Multi-Step-Ahead Prediction with an Long Short-Term Memoery (LSTM) Autoencoder;Applied Sciences;2024-09-01
2. Wind Turbine Blade Fault Diagnosis: Approximate Entropy as a Tool to Detect Erosion and Mass Imbalance;Fractal and Fractional;2024-08-19
3. Health status identification method of PV plant sensors based on abnormal data analysis;Journal of Physics: Conference Series;2024-08-01
4. Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi;Applied Sciences;2024-04-08
5. Prediction and classification of IoT sensor faults using hybrid deep learning model;Discover Applied Sciences;2024-01-20
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