随机不确定性下薄膜电容器的性能退化与剩余寿命评估

赵影; 李志涛; 赵晓宇; 徐晓康; 龚学锐; 张少华; 王叶; 赖伟; 李辉

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随机不确定性下薄膜电容器的性能退化与剩余寿命评估

电力电容器与无功补偿 | 电容器

随机不确定性下薄膜电容器的性能退化与剩余寿命评估
Degradation Trajectory Prediction and Reliability Assessment of Film Capacitors Considering Stochastic Uncertainty
1.中国船舶集团有限公司第七一二研究所，武汉 430000
2.重庆大学输变电装备技术全国重点实验室，重庆 400044
2026年
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赵影, 李志涛, 赵晓宇, 等. 随机不确定性下薄膜电容器的性能退化与剩余寿命评估[J/OL]. 电力电容器与无功补偿, 2026.

ZHAO Ying, LI Zhitao, ZHAO Xiaoyu, et al. Degradation Trajectory Prediction and Reliability Assessment of Film Capacitors Considering Stochastic Uncertainty[J/OL]. Power Capacitor & Reactive Power Compensation, 2026.
赵影, 李志涛, 赵晓宇, 等. 随机不确定性下薄膜电容器的性能退化与剩余寿命评估[J/OL]. 电力电容器与无功补偿, 2026. DOI：

ZHAO Ying, LI Zhitao, ZHAO Xiaoyu, et al. Degradation Trajectory Prediction and Reliability Assessment of Film Capacitors Considering Stochastic Uncertainty[J/OL]. Power Capacitor & Reactive Power Compensation, 2026. DOI：

摘要

金属化薄膜电容器（MFC）在电-热-湿多应力耦合下的容值衰减具有明显的非线性和多阶段的退化特征，同时样件的个体差异与测量误差进一步增加了寿命评估的不确定性。为提高MFC剩余寿命预测的准确度，本文在85℃和85%相对湿度（RH）下开展了交直流叠加电应力加速老化试验，依据容值退化轨迹的阶段特性建立了三阶段非线性Wiener退化模型。联合运用贝叶斯信息准则与迭代搜索方法识别变点位置及各阶段漂移形式，三个阶段的漂移模型分别确定为幂律模型、指数模型和幂律模型。随后结合Rauch-Tung-Striebel平滑与期望最大化算法估计模型参数及构建先验信息，随着序贯观测数据的增加，使用变分贝叶斯推断更新隐状态、随机变量及相关模型参数，进而获得剩余寿命的概率分布表达式。在留一交叉验证结果表明，预测结果随着增量数据的变化逐步接近实际寿命，表明该模型反映了MFC性能退化过程中的阶段变化和样件的个体差异性，验证了序贯数据驱动剩余寿命概率评估方法的有效性。

Abstract

Metallized film capacitors (MFCs) exhibit pronounced nonlinear

multistage capacitance degradation under coupled electrical

thermal

and humidity stresses

while sample-to-sample variability and measurement errors further increase the uncertainty in lifetime assessment. To improve the accuracy of remaining useful life (RUL) prediction for MFCs

accelerated aging tests under superimposed AC–DC electrical stress were conducted at 85 °C and 85% relative humidity (RH)

and a three-stage nonlinear Wiener degradation model was established according to the stage-dependent characteristics of the capacitance degradation trajectories. Change-point locations and stage-specific drift forms were identified using a procedure combining the Bayesian information criterion with iterative search

yielding power-law

exponential

and power-law drift models for the three stages

respectively. Rauch–Tung–Striebel smoothing and the expectation–maximization algorithm were subsequently employed to estimate model parameters and derive prior information. As sequential observations were progressively incorporated

variational Bayesian inference was used to update the latent states

random variables

and associated model parameters

from which the probability distribution of the RUL was derived. Leave-one-out cross-validation results show that the predicted lifetime gradually approaches the actual lifetime as incremental degradation data become available

indicating that the proposed model can capture stage transitions and sample-to-sample variability during MFC degradation and validating the effectiveness of the sequential-data-driven probabilistic RUL assessment method.

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references

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