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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The running gear mechanism is a critical component of high-speed trains, essential for maintaining safety and stability. Malfunctions in the running gear can have severe consequences, making it imperative to assess its condition accurately. Such assessments provide insights into the current operational status, facilitating timely maintenance and ensuring the reliable and safe operation of high-speed trains. Traditional evidential reasoning models for assessing the health of running gear typically require the integration of multiple characteristic indicators, which are often challenging to obtain and may lack comprehensiveness. To address these challenges, this paper introduces a novel assessment model that combines evidential reasoning with multidimensional fault conclusions. This model synthesizes results from various fault diagnoses to establish a comprehensive health indicator system for the running gear. The diagnostic outcomes serve as inputs to the model, which then assesses the overall health status of the running gear system. To address potential inaccuracies in initial model parameters, the covariance matrix adaptation evolution strategy (CMA-ES) algorithm is utilized for parameter optimization. Comparative experiments with alternative methods demonstrate that the proposed model offers superior accuracy and reliability in assessing the health status of high-speed train running gear.

Details

Title
An Evidential Reasoning Assessment Method Based on Multidimensional Fault Conclusion
Author
Gao, Zhi 1   VIAFID ORCID Logo  ; He, Meixuan 2   VIAFID ORCID Logo  ; Zhang, Xinming 3   VIAFID ORCID Logo  ; Gao, Shuo 4 

 Mechanical and Electrical Engineering College, Changchun University of Science and Technology, Changchun 130022, China; [email protected]; School of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, China; [email protected] 
 College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China; [email protected] 
 Mechanical and Electrical Engineering College, Changchun University of Science and Technology, Changchun 130022, China; [email protected]; School of Mechatronic Engineering and Automation, Foshan University, Foshan 528001, China 
 School of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, China; [email protected] 
First page
7689
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3103882383
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.