粒子滤波参数估计方法在齿轮箱剩余寿命预测中的应用研究

针对非线性非高斯系统的剩余寿命（RUL）预测问题，本文提出了一种基于粒子滤波（PF）理论的设备剩余寿命预测方法。首先建立设备的非线性状态空间模型（含有未知的时变参数），然后通过粒子滤波算法估计出设备状态的概率密度函数（PDF），从而根据该PDF计算出设备的RUL。此外，计算设备RUL的期望值和95%置信区间，并对模型的预测效果进行评估，验证预测的有效性和准确性。最后通过齿轮箱的全寿命实验，对本文所提方法的有效性进行实例验证，将实验结果和传统的比例风险模型（PHM）预测结果对比分析，结果表明本文提出的剩余寿命预测方法要优于传统的PHM预测方法。

Residual useful life prediction of gearbox based on particle filtering parameter estimation method

To solve the problem of predicting equipment residual useful life (RUL) which is non-linear and non-Gaussian, a particle filtering framework for system’s RUL prediction is proposed. This framework uses a non-linear state-space model of the system (with unknown time-varying parameters) and a particle filtering (PF) algorithm to estimate the probability density function (PDF) of the state. The state PDF estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the PDF of the remaining useful life (RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of the predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a full life test for a gearbox are used to validate the proposed methodology, and comparisons are made between PHM and PF method, the outcome shows that the PF method has a better effect in the aera of PHM for RUL prediction.