基于粒子群优化粒子滤波的电容剩余寿命预测

Remaining useful life prediction of electrolytic capacitor based on particle swarm optimization particle filter

在电路系统中,电解质电容的故障与否对电路的健康状态有着很大的影响。提出应用一种粒子群优化粒子滤波算法对电解质电容进行状态估计以及剩余寿命的预测。该算法使用NASA已公布的电容数据集,建立一种指数结合多项式的经验退化模型,用粒子群优化算法优化粒子滤波算法中的序贯重采样环节,改善粒子滤波中的粒子贫化问题,实现更准确的电解质电容剩余寿命预测。

In the electrical system,whether the electrolytic capacitors suffer failure influences the health of the circuits enormously.This paper proposes to apply an improved Particle Filter(PF)which is optimized by using the Particle Swarm Optimization algorithm(PSO)to estimate the state and prognostic the Remaining Useful Life(RUL)of the electrolytic capacitors.The algorithm uses the electrolytic capacitors degradation datasets from NASA to establish a model combined the exponential model and the polynomial model,and PSO is applied to optimize the step of sequential important resampling in PF and improve particle impoverishment.The improved prognostic method can predict RUL of the electrolytic capacitor more accurately.