Remaining useful life (RUL) using exponential model (EM) prediction has been a hot research topic in the construction of prognostics health management (PHM) systems. However, in RUL prediction of rolling bearings, the EM 1) depends on the appropriate first prediction time (FPT), 2) requires reliable methods to optimize the model. Therefore, an improved EM is developed to predict the RUL of rolling bearings. Firstly, an adaptive method based on kurtosis and root mean square (RMS) of bearing vibration signals is used to determine the appropriate FPT. Secondly, gradient descent method is used to reliably optimize the EM. A commonly used bearing degradation datasets are analyzed to show the advantages of the present method. Compared with the traditional EM, the method can not only adaptively determine FPT, but also predict RUL more accurately.

使用指数模型（EM）预测的剩余使用寿命（RUL）一直是预后健康管理（PHM）系统构建中的热门研究主题。但是，在滚动轴承的RUL预测中，EM 1）取决于适当的第一预测时间（FPT），2）需要可靠的方法来优化模型。因此，开发了一种改进的EM来预测滚动轴承的RUL。首先，基于峰度和轴承振动信号的均方根（RMS）的自适应方法来确定合适的FPT。其次，使用梯度下降法可靠地优化了电磁场。分析了常用的轴承退化数据集，以显示本方法的优点。与传统的EM相比，该方法不仅可以自适应地确定FPT，而且可以更准确地预测RUL。