Construction and Evolution of Fault Diagnosis Knowledge Graph in Industrial Process
Author:
Affiliation:
1. Computer Integrated Manufacturing System Research Center, College of Electronics and Information Engineering, Tongji University, Shanghai, China
2. Tsinghua Shenzhen International Graduate School, Tsinghua University, Beijing, China
Funder
National Science and Technology Innovation 2030 of China New-Generation Artificial Intelligence Major Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/9717300/09863829.pdf?arnumber=9863829
Reference52 articles.
1. Path-based reasoning approach for knowledge graph completion using CNN-BiLSTM with attention mechanism
2. A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
3. Translating embeddings for modeling multi-relational data;bordes;Proc NIPS,2013
4. Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning
5. Knowledge Graph Completion by Jointly Learning Structural Features and Soft Logical Rules
Cited by 13 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. MultiCogniGraph: A multimodal data fusion and graph convolutional network‐based multi‐hop reasoning method for large equipment fault diagnosis;Computational Intelligence;2024-06
2. Modeling and application of a customized knowledge graph for railway alignment optimization;Expert Systems with Applications;2024-06
3. Research on knowledge graph-driven equipment fault diagnosis method for intelligent manufacturing;The International Journal of Advanced Manufacturing Technology;2024-01-18
4. Leveraging Knowledge Graph Reasoning in a Multihop Question Answering System for Hot Rolling Line Fault Diagnosis;IEEE Transactions on Instrumentation and Measurement;2024
5. A Rolling Bearing Fault Diagnosis Method Based on Multimodal Knowledge Graph;IEEE Transactions on Industrial Informatics;2024
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