基于退化模型动态校准的设备剩余寿命预测方法

任超; 李慧琴; 李天梅; 张建勋; 司小胜

doi:10.7527/S1000-6893.2023.28345

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DOI: https://doi.org/10.7527/S1000-6893.2023.28345

基于退化模型动态校准的设备剩余寿命预测方法

任超 ,
李慧琴 ,
李天梅 ,
张建勋 ,
司小胜

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1. 火箭军工程大学
2. 清华大学
3. 第二炮兵工程大学

收稿日期: 2022-12-01

修回日期: 2023-02-14

网络出版日期: 2023-02-17

基金资助

复杂系统寿命

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Remaining Useful Life Prediction Method for Equipment With Dynamic Calibration of Degradation Model

REN Chao ,
LI Hui-Qin ,
LI Tian-Mei ,
ZHANG Jian-Xun ,
SI Xiao-Sheng

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Received date: 2022-12-01

Revised date: 2023-02-14

Online published: 2023-02-17

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摘要

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，为剩余寿命预测不确定性的量化提供了极大的便利。现有研究中这类方法对设备退化过程建模和预测时，一般采用固定的退化模型函数形式，然后利用设备退化监测数据仅对模型参数进行估计和更新以实现退化模型的校准。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，本文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。

关键词： 剩余寿命; 退化过程模型; 动态校准; Bayesian方法

本文引用格式

任超 , 李慧琴 , 李天梅 , 张建勋 , 司小胜 . 基于退化模型动态校准的设备剩余寿命预测方法[J]. 航空学报, 0 : 0 -0 . DOI: 10.7527/S1000-6893.2023.28345

Abstract

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’s performance degradation variable and provide the probabilistic distribution of the RUL facilitating the uncertainty quantification of the RUL prediction. In existing studies with such methods for degradation modeling and RUL prediction, the fixed functional form of the degradation model is adopted and the model parameters are estimated or updated by the degradation monitoring data of the concerned equipment to perform the model calibration. However, selecting the functional form of the degradation model is itself a challenging problem. More importantly, when the selected functional form of the degradation model is inappropriate, it is difficult and ineffective to calibrate the degradation model simply by updating the model parameters, and the prediction accuracy will be thus affected. In this paper, a RUL prediction method for equipment is developed based on dynamic calibration of degradation model. First, the 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, the parametric model for the degradation prediction errors is established to compensate for the degradation model to calibrate the functional form of the degradation model. At the meanwhile, the parameters of the calibrated model are updated based on the method to achieve the simultaneous calibration of the functional form and parameters of the degradation model. With the calibrated model, the RUL distribution of the equipment is derived for the RUL prediction. Finally, the developed method is validated by the numerical simulations and lithium battery data.

Key words： Remaining useful life; Degradation process model; Dynamic calibration; Bayesian method

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