基于灰色理论的失效机理一致性检验方法

姚军; 王欢; 苏泉

基于灰色理论的失效机理一致性检验方法

北京航空航天大学可靠性与系统工程学院, 北京 100191

详细信息

中图分类号: V216.5
计量
- 文章访问数: 1587
- HTML全文浏览量: 217
- PDF下载量: 848
- 被引次数: 0
出版历程
- 收稿日期: 2012-06-13
- 网络出版日期: 2013-06-30

Consistency identification method of failure mechanism based on grey theory

School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 针对基于加速模型参数不变、基于统计方法及基于试验观察3种常见的失效机理一致性检验方法进行研究,发现3种方法均需要加速试验数据来对失效机理一致性进行检验,不能事先为加速试验提供理论指导;提出了一种基于灰色理论的失效机理一致性检验方法,该方法可用预试验数据进行失效机理一致性检验,为加速试验制定最高应力台阶提供理论指导;结合某型光电编码器预试验数据对该方法进行实例验证,得出175℃附近为失效机理变化点,并与基于统计的方法进行对比;最后,对产品进行失效机理分析,利用扫描电镜分析(SEM,Scanning Electron Microscope)结果验证该方法的正确性.
- 灰色理论 /
- 失效机理 /
- 一致性检验方法 /
- 发光二极管 /
- 光敏三极管
Abstract: A study was made on the consistency identification method of failure mechanism. Three common methods based on the acceleration model parameters unchanged, based on the statistical methods and based on the experimental observation were studied. It was found that the three methods need the accelerated test data for the consistency identification method of failure mechanism, but this could not provide theoretical guidance for the accelerated test. A consistency identification method of failure mechanism based on grey theory was put forward, which can be used for failure mechanism consistency identification by the pre-test data. Combined with the pre-test data of a certain type of photoelectric encoder, the conclusion was drawn that 175℃° was the change point of the failure mechanism using this method. It made a contrast with the method based on statistical. Finally, the failure mechanism analysis for the products was used to verify the correctness of the method.
- grey theory /
- failure mechanism /
- consistency identification method /
- light-emitting diode /
- phototransistor

HTML全文

参考文献(14)

[1]	茆诗松,王玲玲.可靠性统计[M].上海:华东师范大学出版社,1984 Mao Shisong,Wang Lingling.Reliability statistics[M].Shang hai:East China Normal University Press,1984(in Chinese)
[2]	Pan Xiaoxi,Huang Xiaokai,Chen Yunxia,et al.Connotation of failure mechanism consistency and identification method for accelerated testing[C]// Prognostics and System Health Management Conference.[S.l.]:IEEE,2011:1-7
[3]	郭春生,谢雪松,马卫东,等.加速试验中失效机理一致性的判别方法[J].半导体学报,2006,27 (3):560-563 Guo Chunsheng,Xie Xuesong,Ma Weidong,et al.Failure-mechanism identification method in accelerated testing[J].Journal of Semiconductors,2006,27(3):560-563(in Chinese)
[4]	包亦望,苏盛彪,王毅敏,等.钛化物陶瓷的高温蠕变行为与失效机理[J].硅酸盐学报,2002,30(3):300-304 Bao Yiwang,Su Shengbiao,Wang Yimin,et al.High-temperature creep and failure mechanism of ceramics containing titaniferous compound[J].Journal of the Chinese Ceramic Society,2002,30(3):300-304 (in Chinese)
[5]	周源泉,翁朝曦,叶喜涛.论加速系数与失效机理的不变的条件(I)-寿命型随机变的情况[J].系统工程与电子技术,1996(1):55-67 Zhou Yuanquan,Weng Chaoxi,Ye Xitao.Study on accelerated factor and conditionfor constant failure mechanism(Ⅰ)—the case for lifetime is a random variable[J].Journal of Systems Engineering and Electronics,1996(1):55-67(in Chinese)
[6]	Nelson W.Analysis of accelerated life test data-part I:the arrhenius model and graphical methods[J].Transactions on Electrical Insulation,1971,6(4):165-181
[7]	盐见弘.失效物理基础[M].杨家铿译.北京:科学出版社,1982 Shiomi Hiroshi.Basis of failure physics[M].Translated by Yang Jiakeng.Beijing:Science Press,1982(in Chinese)
[8]	Hu J M,Barker D,Dasgupta A,et al.Role of failure-mechanism identification in accelerated testing[C]//Proceedings Annual Reliability and Maintainability Symposium.Las Vegas.NV:IEEE,1992:181-188
[9]	Atwa Y M,El-Saadany E F.Annual wind speed estimation utilizing constrained grey predictor[J].Transaction on Energy Conversion IEEE,2009,24(2):548-550
[10]	邓聚龙.灰色系统基本方法[M].武汉:华中科技大学出版社,2005 Deng Julong.Basic methods of gray system[M].Wuhan:Huazhong University of Science and Technology Press,2005(in Chinese)
[11]	Chang B R,Tsai H F.Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity[J].Expert Systems with Applications,2008,34(2):925-934
[12]	Huang K Y,Jane C J.A hybrid model for stock market forecasting and portfolio selection based on ARX grey system and RS theories[J].Expert Systems with Applications,2009,36(3):5387-5392
[13]	Truong D Q,Ahn K K.Force control for hydraulic load simulator using self-tuning grey predictor-fuzzy PID[J].Mechatronics,2009,19(2):233-246
[14]	Kayacan E,Ulutas B,Kaynak O.Grey system theory-based models in time series prediction[J].Expert Systems with Applications,2010,37(2):1784-1789