A Real-time Reliability Prediction Approach for Analog Circuits Based on Noise-assisted Technique and On-site Data Update

doi:10.3969/j.issn.1000-1093.2019.02.012

Abstract

Abstract: 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 pro- bability 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.Key

Key words: analogcircuit, realtimeprediction, noise-assistedtechnique, on-sitedata, particlefilter, informationfusion

CLC Number:

TN710.4

YAN Liyue, WANG Houjun, LIU Zhen. A Real-time Reliability Prediction Approach for Analog Circuits Based on Noise-assisted Technique and On-site Data Update[J]. Acta Armamentarii, 2019, 40(2): 326-333.

References

［1］ VASAN A S S, LONG B, PECHT M. Diagnostics and prognostics method for analog electronic circuits［J］. IEEE Transactions on Industrial Electronics, 2013, 60(11): 5277-5291.
［2］ VICHARE N M, PECHT M G. Prognostics and health management of electronics［J］. IEEE Transactions on Components and Packaging 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 qualification test scheme based on reliability growth［J］. Acta Armamentarii, 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 tiltrotor aircraft via improved discrete wavelet-OMELM［J］. Acta Armamentarii, 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 and health management system for unmanned ground vehicles［J］. Acta 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 improvement 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 randomization［J］. IEEE Signal Processing Letters, 2008, 15: 709-712.
［9］ CHEN H, VARSHNEY P K, KAY S, et al. Noise enhanced nonparametric detection［J］. IEEE Transactions on Information Theory, 2009, 55(2): 499-506.

［10］ LI X, XIE Y, BI D, et al. Kalman filter based method for fault diagnosis of analog circuits［J］. Metrology and Measurement Systems, 2013, 20(2): 307-322.
［11］ YAN L Y, WANG H J, LIU Z, et al. A novel noise-assisted prognostic method for linear analog circuits［J］. Journal of Electronic Testing, 2017, 33(5): 559-572.
［12］ ALAM M A, AZARIAN M H, OSTERMAN M, et al. Prognostics of failures in embedded planar capacitors using model-based and data-driven approaches［J］. Journal of Intelligent Material Systems and Structures, 2011, 22(12): 1293-1304.
［13］ HE W, WILLIARD N, OSTERMAN M, et al.Remaining useful performance analysis of batteries［C］∥Proceedings of the 2011 IEEE Conference on Prognostics and Health Management. Denver, Colorado, US：IEEE,2011: 602434.
［14］ XING Y J, MA E W M, TSUI K L, et al. An ensemble model for predicting the remaining useful performance of lithium-ion batteries［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 assessment based on gamma process and bayesian estimation［J］. Journal of Astronautics, 2009, 30(4): 1722-1726.(in Chinese)
［16］ LU H, KOLARIK W J, LU S S. Real-time performance reliability 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 evaluation 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 of small 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 of the large complex system based on Bayes theory ［J］.Acta Armamentarii, 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 and prognosis methods based on hidden semi-Markov model［J］. Acta Armamentarii, 2009, 30(1): 69-75. (in Chinese)
［21］ WANG P. Systems reliability prediction based on degradation modeling considering field operating stress scenarios［D］. Newark,NJ,US: Rutgers University, 2003.
［22］ FEDI G, MANETTI S, PICCIRILLI M C, et al. Determination of an optimum set of testable components in the fault diagnosis of analog linear circuits［J］. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1999, 46(7):779-787.
［23］ ORCHARD M E, VACHTSEVANOS G J. A particle-filtering approach 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 the remaining useful life of nonlinear components［J］.Reliability Engineering & System Safety, 2011, 96(3): 403-409.
［25］ ALAM M A, AZARIAN M H, OSTERMAN M, et al. Effectiveness of embedded capacitors in reducing the number of surface mount capacitors for decoupling applications［J］. Circuit World, 2010, 36(1):22-30.

第40卷
第2期2019 年2月兵工学报ACTA
ARMAMENTARIIVol.40No.2Feb.2019

[1]	WANG Bo, JIANG Ping, GUO Bo. Reliability Evaluation of Aerospace Valves Based on Multi-source Information Fusion [J]. Acta Armamentarii, 2022, 43(1): 199-206.
[2]	ZHU Min， XU Aiqiang， XU Qing， LI Ruifeng. Fault Diagnosis of Analog Circuits Based on Improved Multilayer Kernel Extreme Learning Machine [J]. Acta Armamentarii, 2021, 42(2): 356-369.
[3]	LI Hongrui. Neural Network-based Information Fusion Technique for Distributed Passive Sensor [J]. Acta Armamentarii, 2020, 41(1): 95-101.
[4]	GUO Yonghong, SONG Biao, ZHAO Dongyang, LI Xuguang, NAN Huailiang. Research on Extreme Learning Machine Model-based Particle Filter Tracking Method for Device-free Localization [J]. Acta Armamentarii, 2019, 40(8): 1740-1746.
[5]	ZHANG Wei， LIU Xing, XU Ai-qiang， PING Dian-fa. Three-layer Multiple Kernel Fault Diagnosis Model with l_p-norm Constraint for Analog Circuit [J]. Acta Armamentarii, 2018, 39(7): 1352-1363.
[6]	WU Ming, ZHANG Guo-liang, LI Lin-lin, FU Guang-yuan, LI Cheng-jian. Muli-sensor Calibration Optimization Method of Mobile Robot Based on Stationary and Moving Object Observation ConsistencyConstraint [J]. Acta Armamentarii, 2017, 38(8): 1630-1641.
[7]	ZHU Su, BO Yu-ming, HE Liang. Multi-feature Compressive Sensing Target Tracking Algorithm Based on Redundant Dictionary [J]. Acta Armamentarii, 2017, 38(6): 1140-1146.
[8]	MIAO Cheng-lin, LI Tong, LYU Jun， LI Hao. Cooperative Spectrum Sensing Algorithm against Spectrum Sensing Data Falsification Based on Dempster-Shafer Evidence Theory [J]. Acta Armamentarii, 2017, 38(12): 2406-2413.