基于随机相关的电子部件二元加速退化可靠性评估

盖炳良; 滕克难; 王浩伟; 王文双; 陈健; 宦婧

doi:10.13700/j.bh.1001-5965.2019.0130

基于随机相关的电子部件二元加速退化可靠性评估

doi: 10.13700/j.bh.1001-5965.2019.0130

盖炳良^{1, 2},
滕克难^1, ,,
王浩伟¹,
王文双¹,
陈健¹,
宦婧³

1.
海军航空大学烟台 264001
2.
中国人民解放军 91115部队, 舟山 316000
3.
江苏科技大学计算机学院, 镇江 212003

基金项目:

国家自然科学基金 51605487

详细信息

作者简介:
盖炳良男, 博士研究生。主要研究方向:装备可靠性评估、加速试验技术等

滕克难男, 博士, 教授, 博士生导师。主要研究方向:装备延寿理论与技术

通讯作者:
滕克难.E-mail:Tengkn@sina.com

中图分类号: TB114.3
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- 被引次数: 0
出版历程
- 收稿日期: 2019-03-27
- 录用日期: 2019-05-28
- 网络出版日期: 2019-11-20

Reliability assessment for electronic components with bivariate accelerated degradation based on random correlation

1.
Naval Aviation University, Yantai 264001, China
2.
Unit 91115 of the PLA, Zhoushan 316000, China
3.
School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Funds:

National Natural Science Foundation of China 51605487

More Information

Corresponding author: TENG Kenan, E-mail: Tengkn@sina.com

摘要

摘要:
针对加速应力下电子部件二元相关退化可靠性分析难题，提出一种基于随机相关的可靠性分析方法。采用考虑个体差异的Wiener过程模型建立边缘退化过程模型，并基于加速因子不变原则建立了模型参数与加速应力的关系；构建了基于Copula函数的随机相关模型，采用两阶段贝叶斯参数估计方法进行参数估计，综合运用散点图、偏差信息准则（DIC）值以及Kendall τ的非参数估计值等方法进行随机相关模型选择，并采用蒙特卡罗仿真方法进行可靠度计算。最后采用实例验证了所提方法有效性，为考虑个体差异的贮存可靠性评估提供了技术支撑。
- 可靠性评估 /
- 加速退化数据 /
- 二元退化 /
- 随机相关 /
- 电子部件
Abstract:
Targeting at the difficulty of reliability analysis for electronic components with bivariate correlation accelerated degradation data, a reliability assessment method based on random correlation is proposed. The Wiener process model with random effect is used to model the marginal degradation process considering the individual difference, and the relationship between model parameters and acceleration stress is established by using acceleration factor constant principle. Then, a bivariate degradation model with random correlation based on Copula function is established. A two-stage Bayesian method is introduced to facilitate the parameter estimation, and the scatter plots, deviance information criterion (DIC) and the non-parametric estimation of Kendall τ are used for random correlation model selection. The reliability calculation is carried out by Monte Carlo simulation method. Finally, an example is used to verify the effectiveness of the proposed method.The paper has significant meaning for the storage reliability assessment considering individual differences.
- reliability assessment /
- accelerated degradation data /
- bivariate degradation /
- random correlation /
- electronic component

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图 1 二元相关退化建模框架

Figure 1. Framework of bivariate correlation degradation modeling

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图 2 X₁和X₂的随机参数箱线图

Figure 2. Boxplots of random parameters of X₁ and X₂

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图 3 3个加速应力下的边缘退化增量累积分布函数取值的散点图

Figure 3. Scatter plots of CDFs of degradation increments under three accelerated stresses

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图 4 边缘退化增量累积分布函数取值的散点图

Figure 4. Scatter plot of CDFs of degradation increments

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图 5 随机相关性模型参数的箱线图

Figure 5. Boxplots of parameters of random correlation model

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图 6 可靠度曲线

Figure 6. Reliability curves

下载: 全尺寸图片幻灯片

表 1 Copula函数

Table 1. Copula function

Copula函数	分布函数C(u, v; θ)	θ	τ
Frank
Gaussian
Gumbel
Clayton

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表 2 边缘分布参数估计值

Table 2. Parameter estimations of marginal distribution

寿命表征参数	参数	均值	置信区间(置信水平为0.95)	先验
X₁	_RDV(1)	1.338	[0.111 9，2.778]	U(0, 100)
	_RDV(2)	906.2	[689.1，997.3]	U(0, 1 000)
	_RDV(3)	0.530 3	[0.016 53，1.678]	U(0, 100)
	_RDV(4)	0.444 5	[0.018 11，1.206]	U(0, 100)
		0.259 6	[0.158 9，0.359 6]	U(0, 10)
		4.448	[1.288，9.462]	U(0, 10)
X₂	_RDV(1)	2.901	[1.23，4.303]	U(0, 100)
	_RDV(2)	823.4	[465.8，994.4]	U(0, 1 000)
	_RDV(3)	0.651 5	[0.021 36, 1.983]	U(0, 100)
	_RDV(4)	1.058	[0.084 9, 2.11]	U(0, 100)
		0.217	[0.126 9, 0.309 5]	U(0, 10)
		6.027	[1.729, 9.793]	U(0, 10)

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表 3 Copula函数参数估计值

Table 3. Parameter estimations of Copula function

模型	参数	均值	先验	DIC值	τ
Gaussian模型A	θ	0.158 1	U(-1, 1)	179	0.101 1
Frank模型A	θ	2.897	U(0, 100)	-14.07	0.298 1
Gumbel模型A	θ	1.302	U(1, 100)	-11.46	0.232 0
Clayton模型A	θ	0.558	U(0, 100)	-12.18	0.218 2

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表 4 随机相关模型参数估计值

Table 4. Parameter estimations of random correlation models

模型	参数	均值	置信区间(置信水平为0.95)	先验	DIC值
A	θ	2.897	[1.493，4.288]	(0, 100)	-14.07
B	γ_B(1)	1.433	[0.544 7，2.195]	(0, 100)	-13.64
B	γ_B(2)	190.4	[8.855，388.4]	(0, 400)	-13.64
C	a_θ	3.677	[1.615，6.332]	(0, 100)	-16.45
C	b_θ	2.357	[0.228 2，5.605]	(0, 100)	-16.45
D	γ_D(1)	14.2	[0.904 6，43.15]	(0, 100)	-13.61
	γ_D(2)	9 280	[154.6，19 590]	(0, 20 000)
	b_θ	4.29	[0.727 5，9.157]	(0, 100)
E	γ_E(1)	2.706	[0.159 9, 5.845]	(0, 100)	-15.18
	γ_E(2)	1 139	[90.83，1 963]	(0, 2 000)
	a_θ	3.416	[1.628，5.748]	(0, 100)
F	γ_F(1)	3.161	[0.905，6.113]	(0, 100)	-13.66
	γ_F(2)	813.9	[29.4，1 909]	(0, 2 000)
	γ_F(3)	2.193	[0.138 2，4.711]	(0, 100)
	γ_F(4)	830	[58.14，1 471]	(0, 1 500)

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