Forecasting of the Strength in Shock Model

doi:10.12005/orms.2020.0012

Operations Research and Management Science ›› 2020, Vol. 29 ›› Issue (1): 96-98.DOI: 10.12005/orms.2020.0012

• Theory Analysis and Methodology Study • Previous Articles Next Articles

Forecasting of the Strength in Shock Model

ZHANG Quan, LI Yan-jun, HAN Yang

Science College , Qiqihaer University , Qiqihaer 161006, China

Received:2018-10-17 Online:2020-01-25

冲击模型中强度的预测

张权, 李艳君, 韩旸

齐齐哈尔大学理学院,黑龙江齐齐哈尔 161006

作者简介:张权(1978-), 男, 黑龙江齐齐哈尔人, 副教授, 硕士生导师, 博士, 研究方向:可靠性统计;李艳君(1978-), 女, 黑龙江牡丹江人, 讲师, 硕士, 研究方向:数理统计;韩旸(1969-), 男, 黑龙江齐齐哈尔人, 教授, 硕士生导师, 博士后, 研究方向:算法分析。
基金资助:
“十二五”国家科技支撑计划项目(2013BAK12B0803);黑龙江省教育厅项目(135109233);齐齐哈尔大学教研项目(2016016)

Abstract

Abstract: In practical engineering applications, many factors of system themselves and of random environments cause systems to suffer from degradation and shocks. Shocking is a significant cause of system failure and hence has been paid more attention to. In this paper, A forecasting model of the shock strength is presented. Based on the available history of the strength series up to time t, we would forecast the valve of in the minimum mean square error forecasting. The confidence interval for the predicated valve is given in normality assumption. This model will have wide application in many fields. A numerical example is given to illustrate the model.

Key words: the forecast model of ARS(P), the shock strength, the Green function, forecasting

摘要： 提出了冲击强度预测模型。结合历史数据信息,用线性最小方差方法给出了冲击强度未来n步的预测值,并在正态假设下,推导出预测值Y_t+n｜Y_t,Y_t-1,…的置信水平为1-α的置信区间。

关键词: ARS(P)预测模型, 冲击强度, 格林函数, 预测

CLC Number:

O213
O211.62

ZHANG Quan, LI Yan-jun, HAN Yang. Forecasting of the Strength in Shock Model[J]. Operations Research and Management Science, 2020, 29(1): 96-98.

张权, 李艳君, 韩旸. 冲击模型中强度的预测[J]. 运筹与管理, 2020, 29(1): 96-98.

References

[1] Fan J, Ghurkes G, Levine R A. Multicomponent lifetime distribution in the presence of ageing[J]. Journal of Applied Probability, 2000, 37: 521-533.
[2] Gut A. Cumulative shock models[J]. Advances in Applied Probability, 1990, 22: 504-507.
[3] Gut A. Mixed shock models[J]. Bernoulli, 2001, 7: 541-555.
[4] Mallor F, Omey E. Shocks, runs and random sums[J]. Journal of Applied Probability, 2001, 38: 438-448.
[5] Montoro-Cazorla D, Pérez-Ocón R, Segovia M C. Shock and wear models under policy N using phase-type distributions[J]. Applied Mathematical Modelling, 2009, 33: 543-554.
[6] Montoro-Cazorla D, Pérez-Ocón R, Segovia M C. Replacement policy in a system under shocks following a markovian arrival process[J]. Reliability Engineering and System Safety, 2009, 94: 497-502.
[7] Montoro-Cazorla D, Pérez-Ocón R. Shock and wear degradating systems under three types of repair[J]. Applied Mathematics and Computation,2012, 218: 11727-11737.
[8] Zhang Quan, Cui Lirong, Dejing Kong. A study on a stochastic process with state classifications based on sojourn times[J]. Quality Technology & Quantitative Management, 2017, 14: 214-236.
[9] Du Shijia, Zeng Zhiguo , Cui Lirong. Reliability analysis of Markov history-dependent repairable systems with neglected failures[J]. Reliability Engineering and System Safety, 2017, 159: 134-142.
[10] Yuchang Mo, Xing Liudong, Cui Lirong. MDD-based perform ability analysis of multi-state linear consecutive-k-out-of-n: F systems[J]. Reliability Engineering and System Safety, 2017, 166: 124-131.
[11] Montoro-Cazorla D, Pérez-Ocón R. Two shock and wear systems under repair standing a finite number of shocks[J]. European Journal Operational Research, 2011, 214: 298-307.
[12] Montoro-Cazorla D , Pérez-Ocón R. A shock and wear system under environmental conditions subject to internal failures repair and replacement[J]. Reliability Engineering and System Safety, 2012, 99: 55-61.
[13] Tang Y, Lam Y. A δ-shock maintenance model for a deteriorating system[J]. European Journal of Operational Research, 2006, 168: 541-56.

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Forecasting of the Strength in Shock Model

冲击模型中强度的预测

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