针对惯性器件具有非线性退化特性且存在少量历史退化数据的特点，采用非线性退化过程对其寿命进行评估，并给出了适用于非线性退化过程的贝叶斯方法。首先，根据Fisher信息矩阵确定两个参数的无信息先验分布；然后利用退化数据独立增量的似然函数得到参数的分布为高斯-逆伽马分布，利用相容历史数据获得第一次后验分布, 再利用新检测数据得到第二次后验分布;最终实现单台设备的参数与寿命预测结果的实时更新。实验表明，非线性退化过程的评估结果较维纳过程更为准确，所给出的贝叶斯方法能有效实现单台设备的实时预测，可为惯性器件的维护决策提供依据。

Due to the facts that the degradation of inertial device is nonlinear and the historical degradation data is limited, a nonlinear degradation process model is utilized to estimate the Remaining Useful Life (RUL) of inertial device, and a Bayes method suitable for nonlinear degradation process is proposed. At first, non-informative prior distribution of two parameters is obtained by using Fisher information matrix. Then, by using likelihood function of independent increment, it is found that the parameters follow Gaussian-Inverse Gamma distribution. The first posterior distribution is obtained by using compatible historical degradation data and the second posterior distribution is obtained by using new degradation data. The parameters of single device and results of RUL are real-time updated in the end. The experiments show that the assessment result of nonlinear degradation process model is more accurate than the Wiener model. The RUL real-time prediction is achieved by utilizing the Bayes method, which can provide a reference for maintaining the inertial device.