一种新的无失效数据可靠性评估方法研究

doi:10.3969/j.issn.1000-1093.2018.08.020

摘要/Abstract

摘要： 为对高可靠性的电子与机电类产品进行可靠性评估，在无失效数据情况下，提出一种将失效信息引进置信限分析中的修正单侧置信限评估法。通过不同截尾时间对应的产品无失效数据，采用Bayes参数估计得到产品失效率，并根据所提出的方法将时间系数进行等效替换，得到产品可靠度与平均故障间隔时间。以某型机器人步进电机的无失效数据为例，将该方法与其他单侧置信限方法的计算结果进行对比分析，结果表明：在置信水平为0.99条件下，产品平均故障间隔时间为37 080 h，小于其他置信限方法的计算结果；当任务时间达到1 000 h时，采用新方法得到的产品可靠度估计值为0.973. 实例分析验证了新方法在无失效数据产品可靠性评估中的有效性，且可防止产品可靠度与平均故障间隔时间估计结果冒进现象的发生。

关键词: 无失效数据, 可靠性评估, 置信限评估法, Bayes参数估计, 平均故障间隔时间

Abstract: In order to analyze the reliability of the high reliability electronic and electromechanical products, a modified unilateral confidence limit analysis method under the condition of zero-failure data is proposed, which introduces the failure information into the confidence limit analysis. The failure rate of the product is estimated by Bayes parameter, and the data of the product with different truncation times are obtained. The time coefficients are equivalently replaced according to the proposed method, and then the reliability of product and the mean time between failures can be known. Based on the zero-failure data of a robot stepping motor, the calculated results of the proposed method is compared with those of the other unilateral confidence limit methods. The results show that, under the confidence level of 0.99, the mean time between failures of the product is 37 080 h, which is less than the calculated result of other confidence limit methods. The reliability of the product is estimated to be 0.973 by using the proposed new method when the mission time reaches 1 000 h. The example analysis proves the effectiveness of the new method in the reliability evaluation of zero-failure data products, and can prevent the product reliability and the mean time between failures estimation from occurrence.Key

Key words: zero-failuredata, reliabilityestimation, confidencelimitestimationmethod, Bayesianparameterestimation, meantimebetweenfailures

中图分类号:

TB114.35

李海洋，谢里阳，李铭，李强，钟海. 一种新的无失效数据可靠性评估方法研究[J]. 兵工学报, 2018, 39(8): 1622-1631.

LI Hai-yang, XIE Li-yang, LI Ming, LI Qiang, ZHONG Hai. Research on a New Reliability Assessment Method for Zero-failure Data[J]. Acta Armamentarii, 2018, 39(8): 1622-1631.

参考文献

［1］ Morris C, Galuppi B E, Rosenbaum P L. Reliability of family report for the gross motor function classification system[J]. Developmental Medicine & Child Neurology, 2004, 46(7):455-460.
[2］ Papavasiliou A S, Rapidi C A, Rizou C, et al. Reliability of Greek version gross motor function classification system[J]. Brain & Development, 2007, 29(2):79-82.
[3］ Wei C Y, Cai X Q, Liu B, et al. A generic construction of quantum-oblivious-key-transfer-based private query with ideal database security and zero failure[J]. IEEE Transactions on Computers, 2018, 67(1):2-8.
[4］ Martz H F, Waller R A. A Bayesian zero-failure (BAZE) reliabi-lity demonstration testing procedure[J]. Journal of Quality Technology, 1979, 11 (3):128-138.
[5］赵海兵, 程依明. 指数分布场合下无失效数据的统计分析[J]. 应用概率统计, 2004, 20(1):59-65.
ZHAO Hai-bing, CHENG Yi-ming. Statistical analysis about zero-failure data using memoryless property of exponential distribution[J]. Chinese Journal of Applied Probability and Statistics, 2004, 20(1):59-65.(in Chinese)
[6］ Jiang P, Xing Y Y, Jia X, et al. Weibull failure probability estimation based on zero-failure data[J]. Mathematical Problems in Engineering, 2015, 2015(3):1-8.
[7］ Miller K W, Morell L J, Noonan R E, et al. Estimating the probability of failure when testing reveals no failures[J]. IEEE Transactions on Software Engineering, 1992, 18(1):33-43.
[8］贾祥, 王小林, 郭波. 极少失效数据和无失效数据的可靠性评估[J]. 机械工程学报, 2016, 52(2):182-188.
JIA Xiang, WANG Xiao-lin, GUO Bo. Reliability assessment for very few failure data and zero-failure data[J]. Journal of Mechanical Engineering, 2016, 52(2):182-188.(in Chinese)
[9］郭荣化, 吴玉生, 陈庆荣. 定时截尾试验中故障数为零装备的平均无故障间隔时间评估方法研究[J]. 兵工学报, 2011, 32(8): 1036-1040.
GUO Rong-hua，WU Yu-sheng，CHEN Qing-rong. Research on estimation method of MTBF for zero-failure data in reliability time truncated qualification test[J]. Acta Armamentarii, 2011, 32(8): 1036-1040. (in Chinese)

[10］罗巍, 张春华, 谭源源, 等. 基于Bootstrap的可修系统贮存可用度近似置信下限评估方法[J]. 兵工学报, 2010, 31(3): 391-395.
LUO Wei, ZHANG Chun-hua, TAN Yuan-yuan, et al. Estimating method of the approximate confidence lower limit of repairable system storage availability based on Bootstrap[J]. Acta Armamentarii, 2010, 31(3):391-395. (in Chinese)
[11］韩明. 产品无失效数据的综合处理[J]. 机械工程学报, 2003, 39(2):129-132.
HAN Ming. Synthesized process for zero-failure data of the products[J]. Chinese Journal of Mechanical Engineering, 2003, 39(2): 129-132. (in Chinese)
[12］ [ZK(#]Kayis S A. Evaluation of confidence limit estimates of cluster analysis on molecular marker data[J]. Journal of the Science of Food & Agriculture, 2012, 92(4):776-780.
[13］ Zhan W, Niu S W. Analysis of confidence lower limits of reliability and hazard rate for electronic stability control systems[J]. Quality and Reliability Engineering, 2013, 29(5):621-629.
[14］夏新涛, 叶亮, 李云飞, 等. 基于多层自助最大熵法的可靠性评估[J]. 兵工学报, 2016, 37(7):1317-1329.
XIA Xin-tao, YE Liang, LI Yun-fei, et al. Reliability evaluation based on hierarchical bootstrap maximum entropy method[J]. Acta Armamentarii, 2016, 37(7):1317-1329. (in Chinese)
[15］陈家鼎，孙万龙，李补喜.关于无失效数据情形下的置信限[J].应用数学学报， 1995， 18(1)： 90-100.
CHEN Jia-ding，SUN Wan-long，LI Bu-xi. On the confidence limits in the case of no failure data[J]. Acta Mathematicae Applicatae Sinica, 1995, 18(1):90-100. (in Chinese)
[16］韩明. Weibull 分布可靠性参数的置信限[J]. 机械强度, 2009, 31(1):59-62.
HAN Ming. Confidence limits of reliability parameters for Weibull distribution Journal[J]. Journal of Mechanical Strength, 2009, 31(1):59-62. (in Chinese)
[17］ Ali J B, Chebel-Morello B, Saidi L, et al. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J]. Mechanical Systems and Signal Processing, 2015, 56/57:150-172.
[18］ Burke J C, Evans J W. Reliability of wear-out items in electric motors in space — a case study[C]∥Proceedings of Annual Reliability and Maintainability Symposium. Lake Buena Vista, FL, US:IEEE, 2011:1-7.
[19］闵远亮. 电动汽车驱动电机寿命预测及可靠性测试方法的研究[D]. 哈尔滨:哈尔滨工业大学, 2011:18-25.
MIN Yuan-liang. Research on life prediction and reliability testing method of the electric vehicle drive motor[D]. Harbin: Harbin Institute of Technology, 2011:18-25. (in Chinese)
[20］四川省机械工程学会设备维修专业委员会. 机器可靠性[M]. 成都:四川人民出版社, 1983:140-152.
Sichuan Province Equipment Maintenance Professional Committee,Mechanical Engineering Society. Machine reliability[M]. Chengdu:Sichuan People's Publishing House, 1983:140-152. (in Chinese)
[21］杨莳百. 发电系统可靠性分析原理和方法[M]. 北京:水利电力出版社, 1985: 90-104.
YANG Shi-bai. Principle and method of reliability analysis of power generation system[M]. Beijing: China Water & Power Press, 1985: 90-104. (in Chinese)
[22］刘佳昌, 牛天福, 罗恒森. 电机寿命分布的数学模型[J]. 哈尔滨电工学院学报, 1995,18(4):472-476.
LIU Jia-chang, Niu Tian-fu, LUO Heng-sen. Mathematical model of motor life distribution[J]. Journal of Harbin Institute of Electrical Technology, 1995,18(4): 472-476. (in Chinese)
[23］谢里阳. 机械可靠性基本理论与方法[M].第2版. 北京:科学出版社, 2012:21-22.
XIE Li-yang. The basic theory and method of mechanical reliabi-lity[M]. 2nd ed. Beijing:Science Press, 2012:21-22. (in Chinese)
[24］陈家鼎, 孙万龙, 李补喜. 关于无失效数据情形下的置信限[J]. 应用数学学报, 1995, 18(1):90-100.
CHEN Jia-ding, SUN Wan-long, LI Bu-xi. On the confidence limits in the case of no-failure data[J]. Acta Mathematicae Sinica, 1995, 18(1):90-100. (in Chinese)
[25］傅惠民. 不完全数据秩分布理论[J]. 航空学报, 1993, 14(11): 578-584.
FU Hui-min. The theory of incomplete data rank distribution[J]. Acta Aeronautica et Astronautica Sinica, 1993, 14(11): 578-584. (in Chinese)
[26］韩明. 某型发动机无失效数据的可靠性分析[J]. 系统工程与电子技术, 1998(12):103-105.
HAN Ming. Bayesian reliability analysis of zero-failure data of a certain model engine[J]. Systems Engineering and Electronics, 1998(12):103-105. (in Chinese)
[27］韩明, 丁元耀, 陈涛. 指数分布无失效数据的多层Bayes分析[J]. 数理统计与管理, 1998, 17(4）: 24-27.
HAN Ming, DING Yuan-yao， CHEN Tao. The hierarchical Bayesian analysis of exponential distribution of zero-failure data[J]. Journal of Applied Statistics and Management, 1998, 17(4）: 24-27 (in Chinese)
[28］ LIU K, Paulino G H, Gardoni P. Segmental multi-point linearization for parameter sensitivity approximation in reliability analysis[J]. Structural Safety, 2016, 62:101-115.
[29］ Zhang Y M, Yang Z. Reliability-based sensitivity analysis of vehicle components with non-normal distribution parameters[J]. International Journal of Automative Technology, 2005, 10(2):181-194.
[30］ Tasooji A, Ghaffarian R, Rinaldi A. Design parameters influencing reliability of CCGA assembly: a sensitivity analysis[C]∥Proceedings of the Tenth Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronics Systems. San Diego, CA, US: IEEE, 2006:1056-1063.

第39卷
第8期2018 年8月兵工学报ACTA
ARMAMENTARIIVol.39No.8Aug.2018

[1]	纪明，卜珺珺，马聪. 最大熵试验法对航天作动器输出可靠性的评估[J]. 兵工学报, 2022, 43(4): 919-923.
[2]	王博，蒋平，郭波. 基于多源信息融合的航天阀门可靠性评估[J]. 兵工学报, 2022, 43(1): 199-206.
[3]	金俊坤，杨宁国，吕冬雪. 结合单元试验数据的指数型串联系统定时截尾试验可靠性评估方法[J]. 兵工学报, 2019, 40(8): 1756-1760.
[4]	闫华，高黎，王魁，漆磊. 大规模多阶段任务系统马尔可夫可靠性模型的存储和计算[J]. 兵工学报, 2016, 37(9): 1715-1720.
[5]	夏新涛，叶亮，李云飞，常振. 基于多层自助最大熵法的可靠性评估[J]. 兵工学报, 2016, 37(7): 1317-1329.
[6]	张文杰，杨华波，张士峰. 基于Bayes混合验前分布的成败型产品可靠性评估[J]. 兵工学报, 2016, 37(3): 505-511.
[7]	朱赛，蔡金燕，孟亚峰，李丹阳，潘刚. 具有故障细胞的胚胎电子阵列上目标电路评估[J]. 兵工学报, 2016, 37(11): 2120-2127.
[8]	盖京波，孔耀. 基于物理特性参数的可靠性评估[J]. 兵工学报, 2015, 36(6): 1104-1109.
[9]	许玉堂，殷永峰，孙静，张弛，刘斌. 基于时间扩展Petri网的实时嵌入式软件体系结构建模及可靠性评估[J]. 兵工学报, 2015, 36(2): 363-373.
[10]	程红伟，陶俊勇，张云安，陈循. 基于F分布的指数型产品环境因子计算方法研究[J]. 兵工学报, 2012, 33(10): 1217-1221.
[11]	赵建印，刘芳，奚文骏. 退化失效与突发失效共存下产品可靠性模型与评估方法研究[J]. 兵工学报, 2011, 32(9): 1136-1139.
[12]	董海平, 赵霞, 蔡瑞娇. 基于信息量等值的火工品可靠性评估小样本方法[J]. 兵工学报, 2011, 32(5): 554-558.
[13]	张士峰，张虹，刘婷，钱山. 基于对数正态假设的系统可靠性评估及试验设计方法[J]. 兵工学报, 2007, 28(1): 63-67.
[14]	张士峰，樊树江，张金槐. 成败型产品可靠性的Bayes评估[J]. 兵工学报, 2001, 22(2): 238-240.
[15]	罗吉庭、罗强. 21个相同F-S火工品串联系统贮存可靠性评估[J]. 兵工学报, 1997, 18(1): 33-37.