Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vect...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part F, Journal of rail and rapid transit Journal of rail and rapid transit, 2019-08, Vol.233 (7), p.701-714
Main Authors: Liu, Yumei, Qiao, Ningguo, Zhao, Congcong, Zhuang, Jiaojiao, Tian, Guangdong
Format: Article
Language:English
Subjects:
Algorithms
High speed rail
Model accuracy
Particle swarm optimization
Predictions
Regression models
Reliability analysis
Support vector machines
Time series
Vibration
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Summary:Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.
ISSN:0954-4097
2041-3017
DOI:10.1177/0954409718804908