Reliability Prediction of Engineering System Based on Adaptive Particle Swarm Optimization Support Vector Regression

doi:10.3901/JME.2023.14.328

Abstract

Abstract: Aiming at the problem of low reliability prediction accuracy, a support vector regression prediction model is proposed. In the process of reliability prediction, an adaptive particle swarm optimization algorithm that combines sine mapping and adaptive strategies to update inertia weights is developed. By enhancing the local mining capabilities and global search capabilities of the algorithm, it is improved to a certain extent. The accuracy and convergence efficiency of the particle swarm algorithm are verified. Based on 8 benchmark functions, the proposed algorithm is compared and verified with other particle swarm algorithms. The results show that the adaptive particle swarm optimization algorithm has better search capabilities than other algorithms. On this basis, a new adaptive particle swarm optimization-support vector machine regression hybrid reliability prediction model is proposed to adjust the parameters of support vector regression and predict the reliability of turbochargers and industrial robot systems. The results show that the hybrid model can meet the actual engineering accuracy requirements in terms of reliability prediction.

Key words: reliability prediction, support vector machine, particle swarm algorithm, turbocharger, industrial robot

CLC Number:

TH132

ZHOU Ce, BAI Bin, YE Nan. Reliability Prediction of Engineering System Based on Adaptive Particle Swarm Optimization Support Vector Regression[J]. Journal of Mechanical Engineering, 2023, 59(14): 328-338.

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