2018
DOI: 10.1177/0954409718804908
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Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system

Abstract: 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… Show more

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Cited by 8 publications
(2 citation statements)
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“…21,22 Nevertheless, the kernel parameters have a greater impact on SVR-based prognosis model. Genetic algorithm 23 and particle swarm algorithm 24 were used to optimize the kernel parameters and penalty factors to improve the prognosis accuracy. However, the prognosis model based on SVR also has the deficiency that the continuity and correlation between data points of time series is not fully considered.…”
Section: Introductionmentioning
confidence: 99%
“…21,22 Nevertheless, the kernel parameters have a greater impact on SVR-based prognosis model. Genetic algorithm 23 and particle swarm algorithm 24 were used to optimize the kernel parameters and penalty factors to improve the prognosis accuracy. However, the prognosis model based on SVR also has the deficiency that the continuity and correlation between data points of time series is not fully considered.…”
Section: Introductionmentioning
confidence: 99%
“…Wang et al [1][2][3] modeled a gear unit and calculated the vibrations and stresses acting on the gearbox, taking into account the driving torque and the vibration transmitted from the wheel. Liu et al 4 proposed a hybrid model that combined the autoregression and support vector regression models to predict vibration time series data, then verified the model with experimental results of vibrations in gear units of high-speed trains. These studies have made it possible to predict the vibration of the entire gear unit, including the bearings.…”
Section: Introductionmentioning
confidence: 99%