A modified bidirectional long short-term memory neural network for rail vehicle suspension fault detection-Reference-Cited by-同舟云学术

A modified bidirectional long short-term memory neural network for rail vehicle suspension fault detection

Published:2022-12-20 Issue: Volume: Page:1-25
ISSN:0042-3114
Container-title:Vehicle System Dynamics
language:en
Short-container-title:Vehicle System Dynamics

Author:

Chen Yuejian¹^ORCID,Niu Gang¹,Li Yifan²^ORCID,Li Yongbo³

Affiliation:

1. Institute of Rail Transit, Tongji University, Shanghai, People’s Republic of China

2. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of China

3. MIIT Key Laboratory of Dynamics and Control of Complex System, School of Aeronautics, Northwestern Polytechnical University, Xian, People’s Republic of China

Funder

Shanghai Sailing Program

Shanghai Collaborative Innovation Research Center for Multi-network & Multi-modal Rail Transit

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Safety, Risk, Reliability and Quality,Automotive Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/00423114.2022.2158879

Reference38 articles.

1. Fault Diagnosis of High-Speed Train Bogie Based on Synchrony Group Convolutions

2. Fault detection method for railway wheel flat using an adaptive multiscale morphological filter

3. Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear

4. Fault diagnosis of high-speed train suspension systems using multiscale permutation entropy and linear local tangent space alignment

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