引入失效情形下某型液压电机可靠性分析

胡文林; 吕卫民

doi:10.13700/j.bh.1001-5965.2019.0514

引入失效情形下某型液压电机可靠性分析

doi: 10.13700/j.bh.1001-5965.2019.0514

胡文林,
吕卫民^,

海军航空大学岸防兵学院, 烟台 264001

基金项目:

国家自然科学基金 51975580

详细信息

作者简介:
胡文林男, 博士研究生。主要研究方向:装备可靠性评估

吕卫民男, 博士, 教授, 博士生导师。主要研究方向:装备综合保障、可靠性评估

通讯作者:
吕卫民, E-mail: 1846607971@qq.com

中图分类号: V240.2;TJ760.1
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- 被引次数: 0
出版历程
- 收稿日期: 2019-09-18
- 录用日期: 2020-04-10
- 网络出版日期: 2020-10-20

Reliability analysis on one type of hydraulic motor in the case of introducing failure

HU Wenlin,
LYU Weimin^,

Coast Guard College, Naval Aviation University, Yantai 264001, China

Funds:

National Natural Science Foundation of China 51975580

More Information

Corresponding author: LYU Weimin, E-mail: 1846607971@qq.com

摘要

摘要:
针对直接使用无失效数据对装备进行可靠性分析而产生的“冒进”问题，通过引入失效信息对数据进行综合处理，从而对可靠性参数进行合理的估计。以寿命服从指数分布的某型导弹液压电机为例，在其失效率的先验分布为Gamma分布且超参数均服从均匀分布时，证明了失效率在无失效数据时的期望Bayes（E-Bayes）估计法，提出了改进型的截尾试验时间的确定方法，通过引进失效信息，推导了失效率的综合E-Bayes估计法，并给出了可靠度的综合估计法。结合液压电机无失效数据实例，计算得到失效率和可靠度的综合E-Bayes估计，与现有的方法相比，二者的极差分别减小了22.33%和38.02%，说明了所提方法的合理性与可用性。
- 可靠性 /
- 无失效数据 /
- 指数分布 /
- Bayes估计 /
- 综合估计
Abstract:
To solve an aggressive problem of directly using zero-failure data to analyze the reliability of the equipment, the data was comprehensively processed by introducing the failure information, and the reliability parameters could be estimated more reasonably. Taking a certain type of missile hydraulic motor whose life expectancy was exponentially distributed as an example, the Expected Bayes (E-Bayes) estimation of the failure rate in the case of zero-failure data was proved when the prior distribution of the failure rate was the Gamma distribution and the hyper-parameters were uniformly distributed. An improved method was proposed to determine the censoring test time. By introducing the failure information, the comprehensive E-Bayes estimation method of failure rate was derived, and the comprehensive estimation method of the reliability was given. For zero-failure data of the hydraulic motor, the comprehensive E-Bayes estimations of the failure rate and reliability are calculated. Compared with the existing methods, the range between the two is reduced by 22.33% and 38.02%, respectively. The computation results indicate the reasonability and availability of the proposed method.
- reliability /
- zero-failure data /
- exponential distribution /
- Bayes estimation /
- comprehensive estimation

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表 1 液压电机无失效数据^[14]

Table 1. Zero-failure data of hydraulic motor^[14]

截尾试验次数i	截尾试验时间t_i/h	截尾试验样本数n_i
1	145	2
2	270	1
3	369	3
4	720	5
5	1 080	4
6	1 230	3

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表 2 失效率估计的计算结果

Table 2. Calculation results of failure rate estimation 10^-5

失效率	s=50	s=200	s=800	s=1 200	s=2 000	s=3 000	s=4 000	s=6 000	失效率极差
	3.758 8	3.737 8	3.656 8	3.605 3	3.507 9	3.395 3	3.291 8	3.107 3	0.651 5
(0)	2.885 0	2.872 6	2.824 4	2.793 4	2.734 1	2.664 4	2.599 2	2.480 5	0.404 5
(1)	8.655 0	8.617 8	8.473 1	8.380 2	8.202 2	7.993 2	7.797 6	7.441 4	1.213 6
(2)	14.425	14.363	14.122	13.967	13.670	13.322	12.996	12.402	2.023
(3)	20.195	20.108	19.771	19.554	19.138	18.651	18.194	17.363	2.832
	8.655 0	8.617 8	8.473 2	8.380 2	8.202 1	7.993 2	7.797 6	7.441 3	1.213 7
	4.898 7	4.873 9	4.778 1	4.716 9	4.600 8	4.465 7	4.340 8	4.116 3	0.782 4
	6.291 5	6.259 6	6.136 1	6.057 3	5.907 7	5.733 8	5.572 9	5.284 1	1.007 4

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表 3 3种类型可靠度的估计值

Table 3. Estimated values on reliability with three different types

可靠度	s=50	s=200	s=800	s=1 200	s=2 000	s=3 000	s=4 000	s=6 000	可靠度极差
(200)	0.992 5	0.992 6	0.992 7	0.992 8	0.993 0	0.993 2	0.993 4	0.993 8	0.001 3
(200)	0.983 2	0.983 3	0.983 6	0.983 9	0.984 3	0.984 7	0.985 1	0.985 9	0.002 7
(200)	0.990 3	0.990 3	0.990 5	0.990 6	0.990 8	0.991 1	0.991 4	0.991 8	0.001 5
(400)	0.985 1	0.985 2	0.985 5	0.985 7	0.986 1	0.986 5	0.986 9	0.987 6	0.002 5
(400)	0.966 7	0.966 9	0.967 5	0.968 0	0.968 8	0.969 7	0.970 5	0.972 0	0.005 1
(400)	0.980 6	0.980 7	0.981 1	0.981 3	0.981 8	0.982 3	0.982 8	0.983 7	0.003 1
(600)	0.977 7	0.977 8	0.978 3	0.978 6	0.979 2	0.979 8	0.980 4	0.981 5	0.003 8
(600)	0.950 5	0.950 8	0.951 7	0.952 3	0.953 5	0.954 8	0.956 1	0.958 3	0.007 8
(600)	0.971 0	0.971 2	0.971 7	0.972 1	0.972 8	0.973 6	0.974 3	0.975 6	0.004 6
(800)	0.970 4	0.970 5	0.971 2	0.971 6	0.972 3	0.973 2	0.974 0	0.975 4	0.005 0
(800)	0.941 6	0.941 9	0.943 2	0.943 9	0.945 4	0.947 1	0.948 7	0.951 5	0.009 9
(800)	0.961 6	0.961 8	0.962 5	0.963 0	0.963 9	0.964 9	0.965 9	0.967 6	0.006 0
(1 000)	0.963 1	0.963 3	0.964 1	0.964 6	0.965 5	0.966 6	0.967 6	0.969 4	0.006 3
(1 000)	0.927 6	0.928 0	0.929 5	0.930 4	0.932 2	0.934 3	0.936 3	0.939 7	0.012 1
(1 000)	0.952 2	0.952 4	0.953 3	0.953 9	0.955 0	0.956 3	0.957 5	0.959 7	0.007 5

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