IoT-Enabled Fault Prediction and Maintenance for Smart Charging Piles-Reference-Cited by-同舟云学术

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IoT-Enabled Fault Prediction and Maintenance for Smart Charging Piles

Published:2023-12-01 Issue:23 Volume:10 Page:21061-21075
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Dui Hongyan¹^ORCID,Dong Xinghui¹,Chen Liwei²^ORCID,Wang Yujie³

Affiliation:

1. School of Management, Zhengzhou University, Zhengzhou, China

2. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China

3. School of Economics and Management, University of Science and Technology Beijing, Beijing, China

Funder

National Natural Science Foundation of China

Program for Science and Technology Innovation Talents in Universities of Henan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10323344/10149006.pdf?arnumber=10149006

Reference51 articles.

1. Data-driven approach augmented in simulation for robust fault prognosis

2. Industrial IoT Intrusion Detection via Evolutionary Cost-Sensitive Learning and Fog Computing

3. Fault Detection of Electric Vehicle Charging Piles Based on Extreme Learning Machine Algorithm

4. Data-Driven Bayesian-Based Takagi–Sugeno Fuzzy Modeling for Dynamic Prediction of Hot Metal Silicon Content in Blast Furnace

5. Classification of Data Stream in Sensor Network With Small Samples

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