低温阀门无失效数据环节可靠性建模方法研究

卢毅; 郑建明; 赵晨

doi:10.13433/j.cnki.1003-8728.20220119

低温阀门无失效数据环节可靠性建模方法研究

doi: 10.13433/j.cnki.1003-8728.20220119

西安理工大学机仪学院, 西安 710048

详细信息

作者简介:
卢毅(1991-), 博士研究生, 研究生方向为可靠性分析, 821809120@qq.com

通讯作者:
郑建明, 博士生导师, 教授, zjm@xaut.edu.cn

中图分类号: TP273
计量
- 文章访问数: 47
- HTML全文浏览量: 16
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2021-08-23
- 刊出日期: 2023-10-25

A Reliability Modeling Method for Non-failure Data Link of Cryogenic Valve

School of Mechanical and Mechanical Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China

摘要

摘要: 针对无失效数据环节的可靠性评估时，采用单一模型难以同时得到参数的点估计和置信区间估计，而采用不同方法分别进行点估计和区间估计会造成结果的一致性问题, 本文针对在无失效数据情形下，对低温阀门填料环节进行可靠性分析，提出了一种运用多层bayes方法得到产品可靠度的点估计，综合bootstrap抽样法从产品的寿命概率分布中重新抽取样本，与矩法结合得出低温阀门填料环节的可靠度区间估计的可靠性建模方法。该方法根据低温阀门实际运行工况，确定多层bayes方法中取值上限参数c的值，进而得到低温阀门填料环节的寿命概率分布曲线，再利用bootstrap法从寿命概率分布中重新抽取新样本，新样本采用矩法获得低温阀门填料环节可靠度的区间估计，并与置信限方法得到的区间估计进行对比。结果表明：在威布尔分布条件下，新模型提高了可靠度区间估计精度，为无失效数据环节可靠性评估提供了理论基础。
- 无失效数据环节 /
- 多层bayes /
- bootstrap法 /
- 低温阀门可靠性评估
Abstract: In evaluating the reliability of a non-failure data link, it is difficult to estimate the points of parameters and their confidence interval at the same time by using a single model, and different methods for point and interval estimation may cause the inconsistency of results. This paper carried out the reliability analysis of the packing link of a cryogenic valve under the condition of non-failure data. A point estimation method of product reliability was proposed with the multilayer Bayes method. Samples were extracted from product life probability distribution with the bootstrap sampling method. Combined with the moment method, a reliability modeling method for estimating the interval reliability of the packing link of a cryogenic valve is obtained. The method was based on actual operation conditions to determine the value of the range parameter c with the multilayer Bayes method. Thus, the service life of the cryogenic valve′s packing link probability distribution curve was extracted from the life probability distribution of new samples through reusing the bootstrap method. New samples of the cryogenic valve′s packing link reliability interval estimation were obtained with the moment method. The results are compared with the interval estimation by using the confidence limit method. They show that the new model improves the accuracy of reliability interval estimation under the Weibull distribution, providing a theoretical basis for the reliability evaluation of non-failure data links.
- non-failure data link /
- multilayer Bayes theorem /
- bootstrap method /
- reliability evaluation of cryogenic valve

HTML全文

图 1 Bootstrap抽样原理示意图

Figure 1. Schematic diagram of bootstrap sampling principles

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图 2 阀门启闭与加压试验台

Figure 2. Valve opening and closing and pressurization test bed

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图 3 低温性能测试系统原理

Figure 3. Principles of low-temperature performance test system

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图 4 多层Bayes下参数c与p₆的关系

Figure 4. The relationship between c and p₆ parameters under the multilayer Bayes

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图 5 可靠度随启闭次数变化规律

Figure 5. Variation of reliability with opening and closing times

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图 6 Bootstrap抽样次数与区间波动关系

Figure 6. Relationship between bootstrap sampling times and interval fluctuation

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图 7 可靠度随启闭次数变化规律

Figure 7. Variation law of reliability with opening and closing times

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图 8 不同模型下的可靠度区间估计

Figure 8. Interval estimation of reliability under different models

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图 9 不同模型可靠度区间宽度的变化规律

Figure 9. Variation of reliability interval width of different models

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表 1 低温截止阀填料环节无失效试验数据

Table 1. Test data of low-temperature stop valve filling failur

截尾次序i	截尾次数t_i/次	样本数n_i/个	未失效总数s_i/个
1	2 750	2	20
2	2 900	2	18
3	3 050	2	16
4	3 200	2	14
5	3 350	2	12
6	3 500	2	10
7	3 650	2	8
8	3 800	2	6
9	3 950	2	4
10	4 100	2	2

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表 2 不同c值下的多层Bayes累计失效率

Table 2. Cumulative failure rate of multilayer Bayes under different C values

累计失效率	c=2	c=3	c=4	c=5	c=6	c=7
p₁	0.044 3	0.043 2	0.042 1	0.041 1	0.040 1	0.039 1
p₂	0.048 7	0.047 3	0.046 0	0.044 8	0.043 6	0.042 5
p₃	0.053 9	0.052 3	0.050 7	0.049 2	0.047 8	0.046 5
p₄	0.060 4	0.058 4	0.056 4	0.054 6	0.052 9	0.051 3
p₅	0.068 8	0.066 1	0.063 7	0.061 4	0.059 3	0.057 3
p₆	0.079 7	0.076 2	0.073 0	0.070 1	0.067 4	0.064 9
p₇	0.094 9	0.090 0	0.085 6	0.081 7	0.078 2	0.075 0
p₈	0.117 2	0.110 0	0.103 6	0.098 1	0.093 2	0.088 8
p₉	0.153 2	0.141 4	0.131 5	0.123 1	0.115 9	0.109 6
p₁₀	0.221 5	0.199 1	0.181 3	0.166 9	0.155 1	0.145 1

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表 3 多层Bayes下各截尾时刻的累计失效率

Table 3. Cumulative failure rate of each truncated time under the multi-layer Bayes

累计失效率	c=90	c=91	c=92	c=93	c=94	极差
p₁	0.015 6	0.015 5	0.015 4	0.015 3	0.015 2	0.04
p₂	0.016 3	0.016 2	0.016 1	0.016 0	0.015 9	0.04
p₃	0.017 0	0.016 9	0.016 8	0.016 7	0.016 6	0.04
p₄	0.018 0	0.017 8	0.017 7	0.017 6	0.017 5	0.05
p₅	0.019 0	0.018 9	0.018 8	0.018 7	0.018 5	0.05
p₆	0.020 3	0.020 2	0.020 0	0.019 9	0.019 8	0.05
p₇	0.021 9	0.021 7	0.021 6	0.021 4	0.021 3	0.06
p₈	0.024 0	0.023 8	0.023 6	0.023 5	0.023 3	0.07
p₉	0.026 9	0.026 7	0.026 5	0.026 3	0.026 1	0.08
p₁₀	0.031 6	0.031 3	0.031 1	0.030 8	0.030 6	0.10

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