@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 - TEPEN2024-IWFDP",
address = "Switzerland",
}
