基于退化失效与突发失效竞争的导弹剩余寿命预测

doi:10.7527/S1000-6893.2015.0224

Abstract

Abstract:

In order to improve the accuracy of remaining life prediction for missiles, a prediction method, which is based on competing risks of degradation failure and traumatic failure, with the comprehensive utilization of the degradation data and failure time data of missile, is proposed. A condition space model is used to evaluate the degradation rate of the whole missile and then Gamma process is utilized to establish the degradation failure model. Assuming that the probability of traumatic failure depends on the degradation rate of the whole missile, Weibull distribution is used to establish the traumatic failure model. Finally the reliability model based on competing risks is set up. The application to a case validates the effectiveness of the proposed method. This study is of engineering value for accurately predicting the remaining life of missiles and performing condition-based maintenance.

Key words: remaining life, degradation failure, traumatic failure, competing mode, condition space model

CLC Number:

WANG Haowei, XI Wenjun, FENG Yuguang. Remaining life prediction based on competing risks of degradation failure and traumatic failure for missiles[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(4): 1240-1248.

References

[1] LIAO L X, FELIX K. Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction[J]. IEEE Transactions on Reliability, 2014, 63(1):191-207.
[2] SI X S, WANG W B, HU C H, et al. Remaining useful life estimation-A review on the statistical data driven approaches[J]. European Journal of Operational Research, 2011, 213:1-14.
[3] 张小丽, 陈雪峰, 李兵, 等. 机械重大装备寿命预测综述[J]. 机械工程学报, 2011, 47(11):100-116. ZHANG X L, CHEN X F, LI B, et al. Review of life prediction for mechanical major equipments[J]. Journal of Mechanical Engineering, 2011, 47(11):100-116(in Chinese).
[4] RAMASSO E, GOURIVEAU R. Remaining useful life estimation by classification of predictions based on neuro-fuzzy system and theory of belief functions[J]. IEEE Transactions on Reliability, 2014, 63(2):555-566.
[5] LOUTAS T H, ROULIAS D, GEORGOULAS G. Remaining useful life estimation in rolling bearings utilizing data-driven probabilistic e-support vectors regression[J]. IEEE Transactions on Reliability, 2013, 62(4):821-832.
[6] LIAO L X. Discovering prognostic features using genetic programming in remaining useful life prediction[J]. IEEE Transactions on Industrial Electrics, 2014, 61(5):2464-2472.
[7] HUANG W, ASKIN R G. Reliability analysis of electronic devices with multiple competing failure modes involving performance aging degradation[J]. Quality and Reliability Engineering International, 2003, 19:241-254.
[8] 谭源源, 张春华, 陈循. 竞争失效场合步进应力加速试验统计分析[J]. 航空学报, 2011, 32(3):429-437. TAN Y Y, ZHANG C H, CHEN X. Analysis of step stress accelerated testing with competing failure modes[J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(3):429-437(in Chinese).
[9] 张祥坡, 尚建忠, 陈循, 等. 三参数Weibull分布竞争失效场合加速寿命试验统计分析[J]. 兵工学报, 2013, 34(12):1603-1610. ZHANG X P, SHANG J Z, CHEN X, et al. Statistical inference of accelerated life test with competing failures based on three-parameter Weibull distribution[J]. Acta Armamentarii, 2013, 34(12):1603-1610(in Chinese).
[10] 罗湘勇, 黄小凯. 基于多机理竞争退化的导弹贮存可靠性分析[J]. 北京航空航天大学学报, 2013, 39(5):701-705. LUO X Y, HUANG X K. Storage reliability analysis of missile based on multi-mechanism competition degradation model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(5):701-705(in Chinese).
[11] LUO W, ZHANG C H, CHEN X, et al. Accelerated reliability demonstration under competing failure modes[J]. Reliability Engineering and System Safety, 2015, 136:75-84.
[12] PAN Z Q, BALAKRISHNAN N, SUN Q, et al. Bivariate degradation analysis of products based on Wiener processes and Copulas[J]. Journal of Statistical Computation and Simulation, 2013, 83(7):1316-1329.
[13] 张建勋, 胡昌华, 周志杰, 等. 多退化变量下基于Copula函数的陀螺仪剩余寿命预测方法[J]. 航空学报, 2014, 35(4):1111-1121. ZHANG J X, HU C H, ZHOU Z J, et al. Multiple degradation variables modeling for remaining useful life estimation of gyros based on Copula function[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(4):1111-1121(in Chinese).
[14] 张永进, 孙有朝, 陆中. 基于Copula函数的单冷贮备串联结构可靠性分析[J]. 航空学报, 2014, 35(8):2207-2216. ZHANG Y J, SUN Y C, LU Z. Copula function-based reliability analysis of a series system with a single cold standby unit[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(8):2207-2216(in Chinese).
[15] WANG X L, BALAKRISHNAN N, GUO B, et al. Residual life estimation based on bivariate non-stationary Gamma degradation process[J]. Journal of Statistical Computation and Simulation, 2015, 85(2):405-421.
[16] 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. ZHAO J Y, LIU F, XI W J. Reliability model and evaluation method of products in competing failure modes[J]. Acta Armamentarii, 2011, 32(9):1136-1139(in Chinese).
[17] YE Z S, TANG L C, XU H Y. A distribution-based systems reliability model under extreme shocks and natural degradation[J]. IEEE Transactions on Reliability, 2011, 60(1):246-256.
[18] JIANG L, FENG Q, COIT D W. Reliability and maintenance modeling for dependent competing failure processes with shifting failure thresholds[J]. IEEE Transactions on Reliability, 2012, 61(4):932-948.
[19] 王华伟, 高军, 吴海桥. 基于竞争失效的航空发动机剩余寿命预测[J]. 机械工程学报, 2014, 50(6):197-204. WANG H W, GAO J, WU H Q. Residual remaining life prediction based on competing failures for aircraft engines[J]. Journal of Mechanical Engineering, 2014, 50(6):197-204(in Chinese).
[20] WANG Y, PHAM H. Modeling the dependent competing risks with multiple degradation processes and random shock using time-varying Copulas[J]. IEEE Transactions on Reliability, 2012, 61(1):13-22.
[21] ZHANG X P, SHANG J Z, CHEN X, et al. Statistical inference of accelerated life testing with dependent competing failures based on Copula theory[J]. IEEE Transactions on Reliability, 2014, 63(3):764-778.
[22] 徐廷学. 基于定期检测的导弹贮存可靠性研究[J]. 弹箭与制导学报, 2008, 28(1):248-250. XU T X. Study on storage reliability based on periodical test for missile[J]. Journal of Projectiles, Rockets, Missile and Guidance, 2008, 28(1):248-250(in Chinese).
[23] 王浩伟, 徐廷学, 赵建忠. 融合加速退化和现场实测退化数据的剩余寿命预测方法[J]. 航空学报, 2014, 35(12):3350-3357. WANG H W, XU T X, ZHAO J Z. Residual life prediction method fusing accelerated degradation and field degradation data[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(12):3350-3357(in Chinese).
[24] 孙闯, 何正嘉, 张周锁, 等. 基于状态信息的航空发动机运行可靠性评估[J]. 机械工程学报, 2013, 49(6):30-37. SUN C, HE Z J, ZHANG Z S, et al. Operating reliability assessment for aero-engine based on condition monitoring information[J]. Journal of Mechanical Engineering, 2013, 49(6):30-37(in Chinese).
[25] WANG H W, XU T X, MI Q L. Lifetime prediction based on Gamma processes from accelerated degradation data[J]. Chinese Journal of Aeronautics, 2015, 28(1):172-179.
[26] WANG X, XU D. An inverse Gaussian process model for degradation data[J]. Technometrics, 2010, 52(2):188-197.
[27] NTZOUFRAS I. Bayesian modeling using WinBUGS[M]. New York:John Wiley & Sons, 2009.
[28] RIGAT F, MIRA A. Parallel hierarchical sampling:A general-purpose interacting Markov chains Monte Carlo algorithm[J]. Computational Statistics & Data Analysis, 2012, 56(6):1450-1467.
[29] CHERNICK M R. Bootstrap methods:A guide for practitioners and researchers[M]. New York:John Wiley & Sons, 2011.

Remaining life prediction based on competing risks of degradation failure and traumatic failure for missiles

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Recommended Articles

Metrics

Comments

[1]	WANG Haowei, TENG Ke'nan, LI Junliang. Lifetime prediction for missile components based on multiple parameters correlative degrading with random shocks of environmental stresses [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(11): 3404-3412.
[2]	HU Ming-min. EXPLORATION OF THE FATIGUE REMAINING LIFE BASED ON AN EQUIVALENT AVERAGE DAMAGE MODE [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2000, 21(3): 262-266.