A Genetic Algorithm and PCA-Based Feature Selection to Improve the Failure Diagnosis Performance of Railway Vehicle Doors-Reference-Cited by-同舟云学术

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A Genetic Algorithm and PCA-Based Feature Selection to Improve the Failure Diagnosis Performance of Railway Vehicle Doors

Published:2022 Issue: Volume:10 Page:115787-115801
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kang Gil Hyun¹^ORCID,Ki Ho Cheol¹,An Sang Hyun²,Choi Juhee³^ORCID,Kim Chul Su⁴

Affiliation:

1. Industry-Academic Cooperation Foundation, Korea National University of Transportation, Uiwang, South Korea

2. Department of Industrial Engineering, Ajou University, Suwon, South Korea

3. Department of Smart Information and Communication Engineering, Sangmyung University, Cheonan, South Korea

4. School of Railroad Engineering, Korea National University of Transportation, Uiwang, South Korea

Funder

Korea Agency for Infrastructure Technology Advancement (KAIA) by the Ministry of Land, Infrastructure and Transport

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/ielx7/6287639/9668973/09928162.pdf?arnumber=9928162

Reference50 articles.

1. Design of a testing bench for simulating tightened-up operating conditions of train‚Äôs passenger access

2. Robustness study and reliability growth based on exploratory design of experiments and statistical analysis: A case study using a train door test bench;cauffriez;Int J Adv Manuf Technol,2012

3. Support vector machine in machine condition monitoring and fault diagnosis

4. FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS

5. LIII. On lines and planes of closest fit to systems of points in space

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