基于确信可靠性理论的蠕变寿命模型应用研究

doi:10.12305/j.issn.1001-506X.2022.03.38

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (3): 1044-1051.doi: 10.12305/j.issn.1001-506X.2022.03.38

基于确信可靠性理论的蠕变寿命模型应用研究

杨晗, 王浩伟*, 李清荣, 陈敏, 彭博

北京航空航天大学云南创新研究院, 云南昆明 650233

收稿日期:2021-03-10 出版日期:2022-03-01 发布日期:2022-03-10
通讯作者: 王浩伟
作者简介:杨晗(1993—), 女, 助理研究员, 硕士, 主要研究方向为确信可靠性理论与应用、电子产品可靠性与失效分析|王浩伟(1981—), 男, 副研究员, 博士, 主要研究方向为加速试验技术、确信可靠性理论|李清荣(1990—), 男, 工程师, 硕士, 主要研究方向为装备通用质量特性研究、电子产品可靠性与安全性研究、数字图像特征提取分析与应用研究|陈敏(1995—), 女, 助理研究员, 硕士, 主要研究方向为电子及机械产品环境适应性研究、电子产品可靠性与安全性研究|彭博(1986—), 男, 助理研究员, 硕士, 主要研究方向为退化建模与寿命预测、电子产品可靠性设计
基金资助:
国家自然科学基金(51975580);国家自然科学基金(62073009)

Application research of creep life model based on belief reliability theory

Han YANG, Haowei WANG*, Qingrong LI, Min CHEN, Bo PENG

Yunnan Innovation Institute of Beihang University, Kunming 650233, China

Received:2021-03-10 Online:2022-03-01 Published:2022-03-10
Contact: Haowei WANG

摘要/Abstract

摘要：

蠕变持久寿命的评估一直受到理论与工程领域的研究和关注, 基于确信可靠性理论这一可靠性领域的新理论所提出的可靠性的三个科学原理建立了蠕变寿命的确信可靠度模型, 并使用不确定理论表征参数的不确定性。通过案例研究分析了模型参数的不确定性, 并将所得结果与基于失效时间数据的概率分布建模进行了对比。分析认为，基于确信可靠性理论的蠕变寿命模型是一种适用于缺少蠕变试验数据情况下的寿命评估新方法, 可以同时考虑多个不同参数不确定性, 蠕变应力指数的不确定性对蠕变可靠度曲线的趋势有显著影响。

关键词: 确信可靠性, 蠕变寿命模型, 不确定理论, 不确定分布

Abstract:

The evaluation of creep endurance life has always been studied and concerned in the field of theory and engineering. Based on the three scientific principles of reliability proposed by the new theory of reliability field, the confident reliability model of creep life is established, and the uncertainty theory is used to characterize the uncertainty of parameters. The uncertainty of model parameters is analyzed through case study, and the results are compared with the probability distribution modeling based on failure time data. The analysis shows that the creep life model based on the sure reliability theory is a new method for life evaluation in the absence of creep test data. Multiple different parameter uncertainties can be considered at the same time, and the uncertainty of creep stress index has a significant impact on the trend of creep reliability curve.

Key words: belief reliability, creep life model, uncertainty theory, uncertainty distribution

中图分类号:

TB114.3

杨晗, 王浩伟, 李清荣, 陈敏, 彭博. 基于确信可靠性理论的蠕变寿命模型应用研究[J]. 系统工程与电子技术, 2022, 44(3): 1044-1051.

Han YANG, Haowei WANG, Qingrong LI, Min CHEN, Bo PENG. Application research of creep life model based on belief reliability theory[J]. Systems Engineering and Electronics, 2022, 44(3): 1044-1051.

图/表 11

图1

表1

表2

表3

表4

图2

图3

表5

图4

图5

图6

参考文献 32

1	SILVA V D . Mechanics and strength of materials[M]. Berlin: Springer, 2006.
2	ABE F , KERN T U , VISWANATHAN R . Creep-resistant steels[M]. Cambridge: Woodhead Publishing, 2008.
3	朱麟. 高铬耐热钢高温蠕变行为及寿命预测[D]. 西安: 西北大学, 2019.
	ZHU L. Creep behavior and life prediction of high chromium hear resistant steel atelevated temperature[D]. Xi'an: Northwestern University, 2019.
4	HOLZER I , KOZESCHNIK E , CERJAK H . New approach to predict the long-term creep behaviour and evolution of precipitate back-stress of 9%~12% chromium steels[J]. Transactions of the Indian Institute of Metals, 2010, 63 (3): 137- 143.
5	LAESON F R , MILLER J . A time-temperature relationship for rupture and creep stress[J]. Transactions of the ASM, 1952, 74 (5): 765- 775.
6	EVANS M . A new statistical framework for the determination of safe creep life using the theta projection technique[J]. Journal of Materials Science, 2012, 47 (6): 2770- 2781. doi: 10.1007/s10853-011-6106-3
7	EVANS M . Statistical properties of the failure time distribution for 12Cr12Mo14V steels[J]. Journal of Materials Processing Technology, 1995, 54 (4): 171- 180.
8	ZHAO J , XING L , HAI T M . Creep damage model based on Z-parameter and confidence level[J]. Journal of Dalian University of Technology, 2008, 48 (6): 825- 829.
9	KIM W G , PARK J Y , KIM S J , et al. Reliability assessment of creep rupture life for Gr.91 steel[J]. Materials and Design, 2013, 51, 1045- 1051. doi: 10.1016/j.matdes.2013.05.013
10	ZHANG C Y , WEI J S , WANG Z , et al. Creep-based reliability evaluation of turbine blade-tip clearance with novel neural network regression[J]. Materials, 2019, 12 (21): 3552- 3572. doi: 10.3390/ma12213552
11	JIANG C , BAI Y C , HAN X , et al. An efficient reliability-based optimization method for uncertain structures based on non-probability interval model[J]. Computers Materials & Continua, 2010, 18 (1): 21- 42.
12	LV H , YANG K , HUANG X T , et al. Design optimization of hybrid uncertain structures with fuzzy-boundary interval variables[J]. International Journal of Mechanics and Materials in Design, 2021, 17 (1): 201- 224. doi: 10.1007/s10999-020-09523-9
13	WANG W X , XUE H , GAO H S . An effective evidence theory-based reliability analysis algorithm for structures with epistemic uncertainty[J]. Quality and Reliability Engineering International, 2020, 37 (12): 841- 855.
14	SONG L F , QIU Z P . Non-probability reliability optimization design for structures with fuzzy and interval variables[J]. Engineering Mechanics, 2013, 30 (6): 36- 45.
15	YU H P , ZHAO Y , MO L . Fuzzy reliability assessment of safety instrumented systems accounting for common cause fai-lure[J]. IEEE Access, 2020, 8, 135371- 135382. doi: 10.1109/ACCESS.2020.3010878
16	LIU B D . Uncertainty theory[M]. 2nd ed Berlin: Springe, 2007.
17	GHAFFARI H A , LIO W C . Network data envelopment analy- sis in uncertain environment[J]. Computers & Industrial En-gineering, 2020, 148, 106657- 106667.
18	ZHANG B, SANG Z, XUE Y Q, et al. Research on optimization of customized bus routes based on uncertainty theory[EB/OL]. [2021-06-30]. https://doi.org/10.1155/2021/6691299.
19	LI X Y , CHEN W B , LI F R , et al. Reliability evaluation with limited and censored time-to-failure data based on uncertainty distributions[J]. Applied Mathematical Modelling, 2021, 94 (1): 2301- 2314.
20	KANG R , ZHANG Q Y , ZENG Z G , et al. Measuring reliability under epistemic uncertainty: review on non-probabilistic reliability metrics[J]. Chinese Journal of Aeronautics, 2016, 29 (3): 571- 579. doi: 10.1016/j.cja.2016.04.004
21	康锐. 确信可靠性理论与方法[M]. 北京: 国防工业出版社, 2020.
	KANG R . Belief reliability: theory and methodology[M]. Beijing: National Defense Industry Press, 2020.
22	ZENG Z G , KANG R . Uncertainty theory as a basis for belief reliability[J]. Information Sciences: An International Journal, 2018, 36 (12): 486- 502.
23	ZHANG Q Y , KANG R , WEN M L . Belief reliability for uncertain random systems[J]. IEEE Trans.on Fuzzy Systems, 2018, 26 (6): 3605- 3614. doi: 10.1109/TFUZZ.2018.2838560
24	于格, 康锐, 林焱辉, 等. 基于确信可靠度的齿轮可靠性建模与分析[J]. 系统工程与电子技术, 2019, 41 (10): 2385- 2391. doi: 10.3969/j.issn.1001-506X.2019.10.31
	YU G , KANG R , LIN Y H , et al. Reliability modeling and analysis of gear based on belief reliability[J]. Systems Engineering and Electronics, 2019, 41 (10): 2385- 2391. doi: 10.3969/j.issn.1001-506X.2019.10.31
25	王浩伟, 康锐. 基于不确定理论的退化数据分析方法[J]. 系统工程与电子技术, 2020, 42 (12): 2924- 2930. doi: 10.3969/j.issn.1001-506X.2020.12.31
	WANG H W , KANG R . Method of analyzing degradation data based on the uncertainty theory[J]. Systems Engineering and Electronics, 2020, 42 (12): 2924- 2930. doi: 10.3969/j.issn.1001-506X.2020.12.31
26	ZU T P , KANG R , WEN M L , et al. α -S-N curve: a novel S-N curve modeling method under small-sample test data using uncertainty theory[J]. International Journal of Fatigue, 2020, 139, 105725- 105735. doi: 10.1016/j.ijfatigue.2020.105725
27	LI X Y , TAO Z , WU J P , et al. Uncertainty theory based relia- bility modeling for fatigue[J]. Engineering Failure Analysis, 2021, 119, 104931- 104949. doi: 10.1016/j.engfailanal.2020.104931
28	LIU B D . Uncertainty theory: a branch of mathematics for mode-ling human uncertainty[M]. Berlin: Springer, 2010.
29	ZHANG J T , ZHANG Q Y , KANG R . Reliability is a science: a philosophical analysis of its validity[J]. Applied Stochastic Models in Business and Industry, 2019, 35 (2): 1654- 1663.
30	BETTEN J . Creep mechanics[M]. Berlin: Springer, 2002.
31	LIO W C , LIU B D . Uncertain maximum likelihood estimation with application to uncertain regression analysis[J]. Soft Computing, 2020, 24 (4): 322- 334. doi: 10.1007/s00500-020-04951-3
32	朱森第, 方向威, 吴民达, 等. 机械工程材料性能数据手册[M]. 北京: 机械工业出版社, 1995.
	ZHU S D , FANG X W , WU M D , et al. Mechanical enginee-ring material performance data manual[M]. Beijing: Machine Press, 1995.

参数	不确定性类型	满足分布	影响因素
外加应力σ/MPa	固有不确定性	${\mathcal{N}}\left( {e1, d1} \right)$	工况
温度T/K	固有不确定性	${\mathcal{N}}\left( {e2, d2} \right)$	环境
变形量失效阈值ε_th/%	固有不确定性	${\mathcal{N}}\left( {e3, d3} \right)$	材料、工况
常数A	认知不确定性	${\mathcal{N}}\left( {e4, d4} \right)$	材料、认知
蠕变应力指数n	认知不确定性	${\mathcal{N}}\left( {e5, d5} \right)$	蠕变机理、认知
蠕变激活能Q/(J/mol)	认知不确定性	${\mathcal{N}}\left( {e6, d6} \right)$	材料、认知
气体常数R_g/(J/(mol·K))	确定性	8.314	—

参数	应力/MPa
参数	170	196	216	235	255	274
蠕变速率(10^-5%/h)	0.75	1.2	2.6	3.4	4.4	9.5

参数	样本编号
参数	1	2	3	4	5
失效应变量/%	0.24	0.27	0.32	0.32	0.35

参数	e	d	参数获取方式
σ/MPa	173	5	工况、经验
ε_th/%	0.30	0.06	不确定极大似然估计
A′	10^-17	5×10^-19	手册数据拟合、经验
n	5.45	0.5	手册数据拟合、经验

[1]	陈志聪, 康锐, 祖天培, 张清源. 基于技术成熟度的确信可靠性分配方法[J]. 系统工程与电子技术, 2022, 44(1): 327-337.
[2]	李泊远, 康锐, 余丽. 基于概率测度的确信可靠性建模与分析方法[J]. 系统工程与电子技术, 2021, 43(7): 1995-2004.
[3]	张清源, 文美林, 康锐, 李泊远, 余丽. 基于确信可靠性的功能、性能与裕量分析方法[J]. 系统工程与电子技术, 2021, 43(5): 1413-1419.
[4]	王浩伟, 康锐. 基于不确定理论的退化数据分析方法[J]. 系统工程与电子技术, 2020, 42(12): 2924-2930.
[5]	胡林敏, 刘朝彩, 刘思佳. 双重不确定冷贮备系统的可靠性分析[J]. 系统工程与电子技术, 2019, 41(11): 2656-2661.
[6]	孟祥飞, 王瑛, 亓尧, 吕茂隆, 李超. 基于P_EV准则的I-UMOP问题求解方法[J]. 系统工程与电子技术, 2018, 40(2): 338-345.
[7]	龙伟军, 龚树凤, 韩清华, 商妮, 魏海涛. 基于不确定相关机会规划的机会阵方向图综合[J]. 系统工程与电子技术, 2017, 39(1): 49-56.

基于确信可靠性理论的蠕变寿命模型应用研究

Application research of creep life model based on belief reliability theory

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 32

相关文章 7

编辑推荐

Metrics

本文评价

参数	样本编号
参数	1	2	3	4	5	6	7	8
失效时间	79	95	123	134	160	196	250	300