摘 要：鉴于Gamma过程具有平稳、独立增量等退化建模所需的属性，将其用于描述设备退化过程，并针对缺乏故障数据时难于进行剩余寿命预测的问题，利用设备运行中采集的表征其退化状态的大量间接状态参数和少量直接状态参数，建立了基于Gamma退化过程的剩余寿命预测模型；针对经验最大化算法中似然函数难于解析求解的问题，引入粒子滤波算法实现了模型参数估计；最后将模型应用于直升机主减速器行星架的剩余寿命预测，得到了不同时刻的预测结果及95%置信区间，验证了预测模型的有效性和准确性。

Abstract: Gamma process was adopted to items’ degradation process description whereas its smooth and independent increment property which is required for degradation model. Moreover, with the problem of remaining useful life prediction without sufficient failure data, a remaining useful life prediction model based on Gamma degradation process was established using the abundant indirect condition information and few direct condition information that could reflect items’ degradation state. Particle Filters was introduced to Experience Maximization (EM) arithmetic to obtain an analytical result of likelihood function. At last, the prediction model was implemented on the planetary carrier of helicopter main gear-box and had obtained the remaining useful life on 95% confidence interval, the result show that the model is effective.