Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method

Fulong Liu; Honglin Guo; Xiaotao Zhang; Wei Chen; Fengshou Gu

doi:10.1007/978-3-031-70235-8_23

Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method

Fulong Liu, Honglin Guo, Xiaotao Zhang, Wei Chen, Fengshou Gu

Tianjin University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The suspension system is critical to ensure the running safety and comfortability of railway vehicle. This paper employed conventional machine learning method of principal component analysis and support vector machine (PCA-SVM) to diagnose the damper fault, wheel surface fault, roller fault, damper fault coupled with wheel surface fault and damper fault coupled with wheel and roller surface faults. The effectiveness of this method was verified by data obtained from a 1/5^th scaled roller rig. The results shown that the performance of PCA-SVM was acceptable for railway vehicle suspension system monitoring.

Original language	English
Title of host publication	Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic
Subtitle of host publication	TEPEN2024-IWFDP - Volume 1
Editors	Bingyan Chen, Xiaoxia Liang, Tian Ran Lin, Fulei Chu, Andrew D. Ball
Publisher	Springer, Cham
Pages	254-261
Number of pages	8
Volume	170
Edition	1st
ISBN (Electronic)	9783031702358
ISBN (Print)	9783031702341, 9783031702372
DOIs	https://doi.org/10.1007/978-3-031-70235-8_23
Publication status	Published - 3 Sept 2024
Event	TEPEN International Workshop on Fault Diagnostic and Prognostic - Qingdao, China Duration: 8 May 2024 → 11 May 2024

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	170 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	TEPEN International Workshop on Fault Diagnostic and Prognostic
Abbreviated title	TEPEN2024-IWFDP
Country/Territory	China
City	Qingdao
Period	8/05/24 → 11/05/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1007/978-3-031-70235-8_23Licence: Unspecified

Cite this

Liu, F., Guo, H., Zhang, X., Chen, W., & Gu, F. (2024). Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method. In B. Chen, X. Liang, T. R. Lin, F. Chu, & A. D. Ball (Eds.), Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic: TEPEN2024-IWFDP - Volume 1 (1st ed., Vol. 170, pp. 254-261). (Mechanisms and Machine Science; Vol. 170 MMS). Springer, Cham. https://doi.org/10.1007/978-3-031-70235-8_23

Liu, Fulong ; Guo, Honglin ; Zhang, Xiaotao et al. / Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method. Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic: TEPEN2024-IWFDP - Volume 1. editor / Bingyan Chen ; Xiaoxia Liang ; Tian Ran Lin ; Fulei Chu ; Andrew D. Ball. Vol. 170 1st. ed. Springer, Cham, 2024. pp. 254-261 (Mechanisms and Machine Science).

@inproceedings{25eebd8965b84f57b74d7f0ef0b567fb,

title = "Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method",

abstract = "The suspension system is critical to ensure the running safety and comfortability of railway vehicle. This paper employed conventional machine learning method of principal component analysis and support vector machine (PCA-SVM) to diagnose the damper fault, wheel surface fault, roller fault, damper fault coupled with wheel surface fault and damper fault coupled with wheel and roller surface faults. The effectiveness of this method was verified by data obtained from a 1/5th scaled roller rig. The results shown that the performance of PCA-SVM was acceptable for railway vehicle suspension system monitoring.",

keywords = "Condition Monitoring, Machine Learning, PCA, Railway Vehicle Suspension, SVM",

author = "Fulong Liu and Honglin Guo and Xiaotao Zhang and Wei Chen and Fengshou Gu",

note = "Funding Information: This paper is financially supported by the Cooperative Scientific Research Program of Chunhui Projects of Ministry Education of China (Grant No.: HZKY20220603) and the fund of Construction and Practical exploration on Mechatronics Engineering Speciality Virtual Teaching and Research Section in the Era of Intelligent+ (Grant No.: ZD22-02). Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; TEPEN International Workshop on Fault Diagnostic and Prognostic, TEPEN2024-IWFDP ; Conference date: 08-05-2024 Through 11-05-2024",

year = "2024",

month = sep,

day = "3",

doi = "10.1007/978-3-031-70235-8\_23",

language = "English",

isbn = "9783031702341",

volume = "170",

series = "Mechanisms and Machine Science",

publisher = "Springer, Cham",

pages = "254--261",

editor = "Bingyan Chen and Xiaoxia Liang and Lin, \{Tian Ran\} and Fulei Chu and Ball, \{Andrew D.\}",

booktitle = "Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic",

address = "Switzerland",

edition = "1st",

}

Liu, F, Guo, H, Zhang, X, Chen, W & Gu, F 2024, Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method. in B Chen, X Liang, TR Lin, F Chu & AD Ball (eds), Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic: TEPEN2024-IWFDP - Volume 1. 1st edn, vol. 170, Mechanisms and Machine Science, vol. 170 MMS, Springer, Cham, pp. 254-261, TEPEN International Workshop on Fault Diagnostic and Prognostic, Qingdao, China, 8/05/24. https://doi.org/10.1007/978-3-031-70235-8_23

Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method. / Liu, Fulong; Guo, Honglin; Zhang, Xiaotao et al.
Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic: TEPEN2024-IWFDP - Volume 1. ed. / Bingyan Chen; Xiaoxia Liang; Tian Ran Lin; Fulei Chu; Andrew D. Ball. Vol. 170 1st. ed. Springer, Cham, 2024. p. 254-261 (Mechanisms and Machine Science; Vol. 170 MMS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method

AU - Liu, Fulong

AU - Guo, Honglin

AU - Zhang, Xiaotao

AU - Chen, Wei

AU - Gu, Fengshou

N1 - Funding Information: This paper is financially supported by the Cooperative Scientific Research Program of Chunhui Projects of Ministry Education of China (Grant No.: HZKY20220603) and the fund of Construction and Practical exploration on Mechatronics Engineering Speciality Virtual Teaching and Research Section in the Era of Intelligent+ (Grant No.: ZD22-02). Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024/9/3

Y1 - 2024/9/3

N2 - The suspension system is critical to ensure the running safety and comfortability of railway vehicle. This paper employed conventional machine learning method of principal component analysis and support vector machine (PCA-SVM) to diagnose the damper fault, wheel surface fault, roller fault, damper fault coupled with wheel surface fault and damper fault coupled with wheel and roller surface faults. The effectiveness of this method was verified by data obtained from a 1/5th scaled roller rig. The results shown that the performance of PCA-SVM was acceptable for railway vehicle suspension system monitoring.

AB - The suspension system is critical to ensure the running safety and comfortability of railway vehicle. This paper employed conventional machine learning method of principal component analysis and support vector machine (PCA-SVM) to diagnose the damper fault, wheel surface fault, roller fault, damper fault coupled with wheel surface fault and damper fault coupled with wheel and roller surface faults. The effectiveness of this method was verified by data obtained from a 1/5th scaled roller rig. The results shown that the performance of PCA-SVM was acceptable for railway vehicle suspension system monitoring.

KW - Condition Monitoring

KW - Machine Learning

KW - PCA

KW - Railway Vehicle Suspension

KW - SVM

UR - https://www.scopus.com/pages/publications/85204384251

UR - https://doi.org/10.1007/978-3-031-70235-8

U2 - 10.1007/978-3-031-70235-8_23

DO - 10.1007/978-3-031-70235-8_23

M3 - Conference contribution

AN - SCOPUS:85204384251

SN - 9783031702341

SN - 9783031702372

VL - 170

T3 - Mechanisms and Machine Science

SP - 254

EP - 261

BT - Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic

A2 - Chen, Bingyan

A2 - Liang, Xiaoxia

A2 - Lin, Tian Ran

A2 - Chu, Fulei

A2 - Ball, Andrew D.

PB - Springer, Cham

T2 - TEPEN International Workshop on Fault Diagnostic and Prognostic

Y2 - 8 May 2024 through 11 May 2024

ER -

Liu F, Guo H, Zhang X, Chen W, Gu F. Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method. In Chen B, Liang X, Lin TR, Chu F, Ball AD, editors, Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic: TEPEN2024-IWFDP - Volume 1. 1st ed. Vol. 170. Springer, Cham. 2024. p. 254-261. (Mechanisms and Machine Science). Epub 2024 Sept 2. doi: 10.1007/978-3-031-70235-8_23

Condition Monitoring of Railway Vehicle Suspension System Based on PCA-SVM Method

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this