剩余寿命预测是实现随机退化设备健康管理的关键技术。统计数据驱动的方法作为剩余寿命预测领域的典型方法，一般采用随机模型建模设备性能退化变量演变规律，以概率分布的形式给出剩余寿命分布的表达式。然而，退化模型函数形式的选择本身是一个难题，尤其在模型函数形式选择不当时仅通过更新模型参数难以有效实现退化模型的校准，进而影响剩余寿命预测的准确性。鉴于此，提出了一种基于退化模型动态校准的设备剩余寿命预测方法，该方法首先建立了基于非线性扩散过程的设备随机退化过程模型，利用设备的退化监测数据运用Bayesian方法对模型参数进行估计，据此对设备未来退化趋势进行预测；然后，利用退化趋势预测误差建立了模型误差的参数化模型对退化模型进行补偿以校准退化模型函数形式，同时对补偿后的退化模型参数采用Bayesian方法进行更新，由此实现了对设备退化模型函数形式和参数的同时动态校准；在此基础上，推导给出了退化模型动态校准下的设备剩余寿命分布。最后，通过数值仿真和锂电池数据验证了所提方法的实用性和有效性。

Remaining Useful Life （RUL） prediction is the key technique to implement the health management of stochastic degrading equipment. As one of the typical methods for RUL prediction， statistical data-driven methods generally adopt the stochastic model to characterize the evolving progression of the equipment performance degradation variable and provide the probabilistic distribution of the RUL to facilitate the uncertainty quantification of the RUL prediction. However， selecting the functional form of the degradation model is itself a challenging problem. More importantly， inappropriate selection of the functional form for the degradation model leads to difficult and ineffective calibration of the degradation model simply by updating the model parameters， thus affecting prediction accuracy. In this paper， an equipment RUL prediction method is developed based on the dynamic calibration of degradation models. First， a stochastic degradation model is constructed based on the nonlinear diffusion process and the model parameters are estimated through the degradation monitoring data of the equipment to predict the future degradation trend. Then， a parametric model for the degradation prediction errors is established to compensate for the degradation model to calibrate its functional form. Meanwhile， the calibrated model parameters are updated based on the Bayesian method to achieve simultaneous calibration of the functional form and the degradation model parameters. With the calibrated model， the equipment RUL distribution is derived for the RUL prediction. Finally， the developed method is validated by numerical simulations and lithium battery data.