一种结合噪声辅助技术和现场数据的模拟电路实时可靠度预测方法
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摘要
信号处理技术和信息融合技术是实时可靠度预测的关键。针对传统方法通常适用于特定的随机过程和数据分布的缺陷,提出一种新的实时可靠度预测方法。该方法采用一种基于卡尔曼滤波的噪声辅助技术来计算表征系统性能退化趋势的故障指示器,利用粒子滤波技术外推电路系统的伪失效性能,采用基于贝叶斯估计方法的信息融合技术更新性能分布的时变参数,从而预测电路的实时可靠度。引入基于真实的嵌入式平面电容器来加速退化实验所统计产生的电容器失效物理模型,并以真实数据代替理想仿真假设数据的例子,验证了结合噪声辅助技术和现场数据的实时可靠度预测方法的有效性。结果表明,现场数据信息越多,电路的实时可靠度预测准确性越高。
Signal processing and information fusion technologies are the key to real-time reliability prediction. The traditional real-time reliability analysis methods are based on specific random process and probability distribution. A new real-time reliability prediction method is proposed. The proposed method uses a Kalman filter-based noise-assisted technique to calculate the fault indicators that characterize system performance degradation trend. On this basis,the particle filter technology is used to extrapolate the pseudo-failure performance of circuit system,and then the Bayesian inference as an information fusion method is introduced to update the time-varying parameters of performance distribution,thus predicting the real-time reliability of the circuit. The failure physical model of an embedded planar capacitor based on the real acceleration degradation experiment is introduced,and the effectiveness of this real-time prediction method combined with noise-assisted technology and on-site data is verified by using real data instead of ideal simulation hypothetical data. The result shows that the more the on-site data information is,the higher the prediction accuracy of circuit reliability is.
Signal processing and information fusion technologies are the key to real-time reliability prediction. The traditional real-time reliability analysis methods are based on specific random process and probability distribution. A new real-time reliability prediction method is proposed. The proposed method uses a Kalman filter-based noise-assisted technique to calculate the fault indicators that characterize system performance degradation trend. On this basis,the particle filter technology is used to extrapolate the pseudo-failure performance of circuit system,and then the Bayesian inference as an information fusion method is introduced to update the time-varying parameters of performance distribution,thus predicting the real-time reliability of the circuit. The failure physical model of an embedded planar capacitor based on the real acceleration degradation experiment is introduced,and the effectiveness of this real-time prediction method combined with noise-assisted technology and on-site data is verified by using real data instead of ideal simulation hypothetical data. The result shows that the more the on-site data information is,the higher the prediction accuracy of circuit reliability is.
引文
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[18]邱荣华,张敏,王锦,等.小子样高速电主轴的Bayes可靠性分析[J].机械设计,2018,35(2):67-71.QIU R H,ZHANG M,WANG J,et al. Reliability analysis ofsmall sample high-speed motorized spindle base on Bayes[J].Journal of Machine Design,2018,35(2):67-71.(in Chinese)
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[20]胡海峰,安茂春,秦国军,等.基于隐半Markov模型的故障诊断和故障预测方法研究[J].兵工学报,2009,30(1):69-75.HU H F,AN M C,QIN G J,et al. Study on fault diagnosis andprognosis methods based on hidden semi-Markov model[J]. ActaArmamentarii,2009,30(1):69-75.(in Chinese)
[21] WANG P. Systems reliability prediction based on degradationmodeling considering field operating stress scenarios[D]. New-ark,NJ,US:Rutgers University,2003.
[22] FEDI G,MANETTI S,PICCIRILLI M C,et al. Determination ofan optimum set of testable components in the fault diagnosis ofanalog linear circuits[J]. IEEE Transactions on Circuits and Sys-tems I:Fundamental Theory and Applications,1999,46(7):779-787.
[23] ORCHARD M E,VACHTSEVANOS G J. A particle-filtering ap-proach for on-line fault diagnosis and failure prognosis[J].Transactions of the Institute of Measurement and Control,2009,31(3/4):221-246.
[24] ZIO E,PELONI G. Particle filtering prognostic estimation of theremaining useful life of nonlinear components[J]. Reliability En-gineering&System Safety,2011,96(3):403-409.
[25] ALAM M A,AZARIAN M H,OSTERMAN M,et al. Effective-ness of embedded capacitors in reducing the number of surfacemount capacitors for decoupling applications[J]. Circuit World,2010,36(1):22-30.
[2] VICHARE N M,PECHT M G. Prognostics and health manage-ment of electronics[J]. IEEE Transactions on Components andPackaging Technologies,2006,29(1):222-229.
[3]李大伟,阮旻智,尤焜.基于可靠性增长的武器系统可靠性鉴定试验方案研究[J].兵工学报,2017,38(9):1815-1821.LI D W,RUAN M Z,YOU K. Weapon system reliability qualifi-cation test scheme based on reliability growth[J]. Acta Armamen-tarii,2017,38(9):1815-1821.(in Chinese)
[4]严峰,陈晓,王新民,等.改进的离散小波-优化极限学习机在倾转旋翼机故障诊断中的应用[J].兵工学报,2014,35(11):1914-1921.YAN F,CHEN X,WANG X M,et al. Fault diagnosis of tiltrotoraircraft via improved discrete wavelet-OMELM[J]. Acta Arma-mentarii,2014,35(11):1914-1921.(in Chinese)
[5]卢玉传,江磊,赵洪雷.地面无人车辆故障预测与健康管理系统研究[J].兵工学报,2014,35(增刊1):68-73.LU Y C,JIANG L,ZHAO H L. Research on fault prediction andhealth management system for unmanned ground vehicles[J]. Ac-ta Armamentarii,2014,35(S1):68-73.(in Chinese)
[6]涂宏茂,孙志礼,钱云鹏,等.引信微机电安全系统的可靠性分析与改进[J].兵工学报,2017,38(4):664-672.TU H M,SUN Z L,QIAN Y P,et al. Reliability analysis and im-provement of MEMS-based safety and arming device in fuze[J].Acta Armamentarii,2017,38(4):664-672.(in Chinese)
[7] ANDO B,GRAZIANI S. Adding noise to improve measurement[J]. IEEE Instrumentation&Measurement Magazine,2001,4(1):24-31.
[8] KAY S. Noise enhanced detection as a special case of randomiza-tion[J]. IEEE Signal Processing Letters,2008,15:709-712.
[9] CHEN H,VARSHNEY P K,KAY S,et al. Noise enhanced non-parametric detection[J]. IEEE Transactions on Information Theo-ry,2009,55(2):499-506.
[10] LI X,XIE Y,BI D,et al. Kalman filter based method for faultdiagnosis of analog circuits[J]. Metrology and Measurement Sys-tems,2013,20(2):307-322.
[11] YAN L Y,WANG H J,LIU Z,et al. A novel noise-assistedprognostic method for linear analog circuits[J]. Journal of Elec-tronic Testing,2017,33(5):559-572.
[12] ALAM M A,AZARIAN M H,OSTERMAN M,et al. Prognos-tics of failures in embedded planar capacitors using model-basedand data-driven approaches[J]. Journal of Intelligent MaterialSystems and Structures,2011,22(12):1293-1304.
[13] HE W,WILLIARD N,OSTERMAN M,et al. Remaining usefulperformance analysis of batteries[C]∥Proceedings of the 2011IEEE Conference on Prognostics and Health Management. Den-ver,Colorado,US:IEEE,2011:602434.
[14] XING Y J,MA E W M,TSUI K L,et al. An ensemble modelfor predicting the remaining useful performance of lithium-ion bat-teries[J]. Microelectronics Reliability,2013,53(6):811-820.
[15]李常有,徐敏强,郭耸,等.基于Gamma过程及贝叶斯估计的实时可靠性评估[J].宇航学报,2009,30(4):1722-1726.LI C Y,XU M Q,GUO S,et al. Real-time reliability assess-ment based on gamma process and bayesian estimation[J]. Jour-nal of Astronautics,2009,30(4):1722-1726.(in Chinese)
[16] LU H,KOLARIK W J,LU S S. Real-time performance reliabili-ty prediction[J]. IEEE Transactions on Reliability,2001,50(4):353-357.
[17]赵炤,董豆豆,周经伦,等.基于贝叶斯方法的退化失效型产品实时可靠性评估[J].国防科技大学学报,2007,29(6):116-120.ZHAO Z,DONG D D,ZHOU J L,et al. Real-time reliability e-valuation for degradation failure product based on Bayes method[J]. Journal of National University of Defense Technology,2007,29(6):116-120.(in Chinese)
[18]邱荣华,张敏,王锦,等.小子样高速电主轴的Bayes可靠性分析[J].机械设计,2018,35(2):67-71.QIU R H,ZHANG M,WANG J,et al. Reliability analysis ofsmall sample high-speed motorized spindle base on Bayes[J].Journal of Machine Design,2018,35(2):67-71.(in Chinese)
[19]连可,龙兵,王厚军.基于贝叶斯最大后验概率准则的大型复杂系统故障诊断方法研究[J].兵工学报,2008,29(3):352-356.LIAN K,LONG B,WANG H J. A fault diagnosis approach ofthe large complex system based on Bayes theory[J]. Acta Arma-mentarii,2008,29(3):352-356.(in Chinese)
[20]胡海峰,安茂春,秦国军,等.基于隐半Markov模型的故障诊断和故障预测方法研究[J].兵工学报,2009,30(1):69-75.HU H F,AN M C,QIN G J,et al. Study on fault diagnosis andprognosis methods based on hidden semi-Markov model[J]. ActaArmamentarii,2009,30(1):69-75.(in Chinese)
[21] WANG P. Systems reliability prediction based on degradationmodeling considering field operating stress scenarios[D]. New-ark,NJ,US:Rutgers University,2003.
[22] FEDI G,MANETTI S,PICCIRILLI M C,et al. Determination ofan optimum set of testable components in the fault diagnosis ofanalog linear circuits[J]. IEEE Transactions on Circuits and Sys-tems I:Fundamental Theory and Applications,1999,46(7):779-787.
[23] ORCHARD M E,VACHTSEVANOS G J. A particle-filtering ap-proach for on-line fault diagnosis and failure prognosis[J].Transactions of the Institute of Measurement and Control,2009,31(3/4):221-246.
[24] ZIO E,PELONI G. Particle filtering prognostic estimation of theremaining useful life of nonlinear components[J]. Reliability En-gineering&System Safety,2011,96(3):403-409.
[25] ALAM M A,AZARIAN M H,OSTERMAN M,et al. Effective-ness of embedded capacitors in reducing the number of surfacemount capacitors for decoupling applications[J]. Circuit World,2010,36(1):22-30.