基于时间应力分析的BIT降虚警与故障预测技术研究
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摘要
机内测试(Built-in Test, BIT)虚警率高和部件故障预测难一直是故障测试与诊断领域所面临的两个共性问题,阻碍着BIT及故障预测系统效能的充分发挥和更广泛的应用。尤其是近年来,故障预测与健康管理(Prognostics and Health Management, PHM)等新概念的提出对如何降低BIT虚警率、提高部件的故障预测能力提出了更高的要求。
时间应力是指设备在生产、运输和工作过程中所承受的各种环境应力和工作应力的时间历程。研究表明：实时应力是导致系统暂时失效进而造成BIT虚警的重要因素,时间应力历程是导致部件性能退化甚至失效的直接外因,对时间应力进行分析可以有效减少BIT虚警和实现部件的故障预测。
本文针对目前BIT虚警率高和部件故障预测难的问题,系统地分析时间应力诱发故障和BIT虚警的机理与规律,对基于时间应力分析的BIT降虚警技术和故障预测技术进行深入研究。
论文的主要研究内容包括：
1.时间应力诱发故障和BIT虚警的机理分析与建模
系统地分析了时间应力诱发故障和BIT虚警的机理和一般规律；建立了基于支持向量机(Support Vector Machine, SVM)的时间应力与BIT虚警的关联模型,描述了时间应力大小与BIT虚警的关联关系。建立了基于随机Petri网的时间应力与损伤关系的动态描述模型,获得时间应力历程与损伤问的转化关系。建立了基于损伤的故障演化隐马尔可夫模型(Hidden Markov Model, HMM),描述了损伤与故障间的演化关系。
2.基于时间应力分析的BIT降虚警技术研究
(1)提出了基于故障模式、影响及应力分析(Failure Mode, Effects and Stress Analysis, FMESA)和多元Logistic回归的关联分析方法,从定性和定量两个角度分析导致BIT虚警的主要时间应力因素和相应的影响程度。针对时间应力导致BIT虚警的主要应力区域确定的问题,提出了基于SVM的关联阈值优化选取方法,获得了时间应力导致BIT虚警的时间应力上下限阈值。
(2)提出了基于时间应力分析的SVM虚警识别方法,能有效识别关联区域内由时间应力造成的虚警。由于该方法不能有效识别关联区域内的真实故障和关联区域外的虚警,分别提出了基于时间应力分析的SVM-HMM虚警识别方法和基于核主元聚类分析(Kernel Based Principal Component Clustering Analysis, KPCCA)模型和HMM串联的虚警识别方法。最后,综合上述三种情形,提出了基于时间应力分析的SVM-KPCCA-HMM综合虚警识别方法,可在时间应力的全区域内有效识别BIT虚警和真实故障。
3.基于时间应力分析的故障预测技术研究
(1)针对如何将部件所承受的应力历程转化为损伤的问题,以被监控对象的失效物理模型为基础,提出了基于多组件寿命消耗监控(Multi-component Life Consumption Monitoring, MLCM)的累积损伤信息提取方法,并以典型的连接组件和分离组件为对象对该方法的技术步骤进行了具体研究,结果表明,该方法可有效地将部件所承受的应力历程转化为部件的累积损伤度量。
(2)综合考虑历史变化趋势和应力突变的影响,提出了基于多点损伤动态优化自回归(Auto-Regression, AR)模型的故障预测方法。由于累积损伤分析和计算等过程中存在不确定性和随机性,为进一步提高组件故障预测的准确性,提出了基于优化AR-HMM的故障预测方法。验证结果表明,该方法具有较高的预测置信度。
4.基于时间应力分析的BIT降虚警和故障预测技术的应用与验证
以某直升机航向姿态系统为对象,设计并实现了具有BIT虚警识别能力和关键部件故障预测能力的PHM系统,并通过理论分析和试验对本文所研究的虚警识别方法和故障预测方法进行了验证。结果表明,本文所研究方法具有较好的虚警识别和故障预测效果。
The high False Alarm Rate (FAR) in Built-in Test (BIT) and low fault prognostics ability of equipments are two common key problems in the fault diagnosis field. They directly prevent BIT and fault prognostics system from more extensive application. In recent years, with some new conceptions such as Prognostics and Health Management (PHM) appearing, how to reduce FAR of BIT and improve the fault prognostics ability become more and more important.
The time stress include all kinds of environment and operating stress such as shock, vibration, temperature and so on that the equipment suffered in the manufacture, transport and operating process. On the one hand, the real-time time stress is the main factor that disables system temporarily and incurs the BIT false alarms. On the other hand, the time stress directly induce degradation or deviation of the components from an expected normal condition. Therefore, analyzing the time stress can reduce false alarms of the BIT and improve fault prognostics ability effectively.
This paper is aiming to solve the two problems of BIT's high FAR and equipment's low fault prognostics ability in fault diagnosis field. The mechanisms of the faults and BIT false alarms induced by time stress are analyzed systematically. Then, the BIT false alarm reducing and fault prognostics technologies based on time stress analysis are studied.
The main contents of the dissertation are as follows.
1. The mechanisms of how the time stress induces faults and BIT false alarms are analyzed systematically and some relative models are built.
Firstly, the mechanisms of the faults and BIT false alarms induced by time stress are analyzed in detail. In order to analyze the relationship between time stress and BIT fault alarm, a relevance analysis model based on Support Vector Machine (SVM) is built. The model can acquire the main time stress region that induces the BIT false alarms. Secondly, a dynamic description damage model is researched based on Generalized Stochastic Petri Nets (GSPN). This model can analyze the evolution relationship between time stress and component's damage. Finally, a probability fault evolution model based on Hidden Markov Model (HMM) is studied. This model can analyze the evolution process of component's fault.
2. The BIT false alarm reducing technology based on time stress analysis is studied.
(1) Two relevance analysis methods between time stress and BIT false alarm based on Failure Mode, Effect and Stress Analysis (FMESA) and logistic regression are studied. These two methods can analyze the main stress factor that induce BIT false alarm from qualitative and quantitative aspects. In order to acquire the optimization threshold values of the relevance analysis model between time stress and BIT false alarm, the threshold values optimization selection method is discussed in detail.
(2) A false alarm recognition method based on SVM and time stress analysis is studied, and it can recognise the false alarms in the time stress relevance region. But this method can not recognise the real faults in the time stress relevance region and the false alarms out of the time stress relevance region effectively. In order to solve the first problem, a false alarm recognition method based on SVM-HMM and time stress analysis is proposed. In order to solve the second problem, a false alarm recognition method based on Kernel based Principal Component Clustering Analysis (KPCCA) and HMM is studied. Finally, a synthetical false alarm recognition method based on SVM-KPCCA-HMM and time stress analysis is proposed. And it can recognise the false alarms and real faults in the whole time stress region.
3. The fault prognostics technology based on time stress analysis is studied.
(1) An accumulative damage assessment method based on component's failure models and Multi-component Life Consumption Monitoring (MLCM) is studied. Then, the accumulative damage assessment methods of joint and separate components are studied in detail. It can transform the time stress suffered in the life time into equipment's damage.
(2) Considering the history current of damage and the abrupt change affection of time stress, a fault prognostics method based on dynamic optimization auto-regression (AR) model and multipoint damages is studied. Because there are some uncertainties in the damage assessment process, a fault prognostics method based on optimization AR model and HMM is proposed. This method can improve the accuracy. The experimental results show that this method has a higher degree of confidence.
4. The BIT false alarm reducing and fault prognostics technologies based on time stress are applied and validated.
In order to evaluate the efficiency of the BIT false alarm reducing and fault prognostics technologies, a PHM system for heading attitude system of the helicopter is built and implemented. Then, some time stress experimental studies on the heading attitude system are executed. The results show that BIT false alarm reducing technology in this paper can reduce false alarms of BIT effectively, and the fault prognostics technology has a good fault prognostics effect.
时间应力是指设备在生产、运输和工作过程中所承受的各种环境应力和工作应力的时间历程。研究表明：实时应力是导致系统暂时失效进而造成BIT虚警的重要因素,时间应力历程是导致部件性能退化甚至失效的直接外因,对时间应力进行分析可以有效减少BIT虚警和实现部件的故障预测。
本文针对目前BIT虚警率高和部件故障预测难的问题,系统地分析时间应力诱发故障和BIT虚警的机理与规律,对基于时间应力分析的BIT降虚警技术和故障预测技术进行深入研究。
论文的主要研究内容包括：
1.时间应力诱发故障和BIT虚警的机理分析与建模
系统地分析了时间应力诱发故障和BIT虚警的机理和一般规律；建立了基于支持向量机(Support Vector Machine, SVM)的时间应力与BIT虚警的关联模型,描述了时间应力大小与BIT虚警的关联关系。建立了基于随机Petri网的时间应力与损伤关系的动态描述模型,获得时间应力历程与损伤问的转化关系。建立了基于损伤的故障演化隐马尔可夫模型(Hidden Markov Model, HMM),描述了损伤与故障间的演化关系。
2.基于时间应力分析的BIT降虚警技术研究
(1)提出了基于故障模式、影响及应力分析(Failure Mode, Effects and Stress Analysis, FMESA)和多元Logistic回归的关联分析方法,从定性和定量两个角度分析导致BIT虚警的主要时间应力因素和相应的影响程度。针对时间应力导致BIT虚警的主要应力区域确定的问题,提出了基于SVM的关联阈值优化选取方法,获得了时间应力导致BIT虚警的时间应力上下限阈值。
(2)提出了基于时间应力分析的SVM虚警识别方法,能有效识别关联区域内由时间应力造成的虚警。由于该方法不能有效识别关联区域内的真实故障和关联区域外的虚警,分别提出了基于时间应力分析的SVM-HMM虚警识别方法和基于核主元聚类分析(Kernel Based Principal Component Clustering Analysis, KPCCA)模型和HMM串联的虚警识别方法。最后,综合上述三种情形,提出了基于时间应力分析的SVM-KPCCA-HMM综合虚警识别方法,可在时间应力的全区域内有效识别BIT虚警和真实故障。
3.基于时间应力分析的故障预测技术研究
(1)针对如何将部件所承受的应力历程转化为损伤的问题,以被监控对象的失效物理模型为基础,提出了基于多组件寿命消耗监控(Multi-component Life Consumption Monitoring, MLCM)的累积损伤信息提取方法,并以典型的连接组件和分离组件为对象对该方法的技术步骤进行了具体研究,结果表明,该方法可有效地将部件所承受的应力历程转化为部件的累积损伤度量。
(2)综合考虑历史变化趋势和应力突变的影响,提出了基于多点损伤动态优化自回归(Auto-Regression, AR)模型的故障预测方法。由于累积损伤分析和计算等过程中存在不确定性和随机性,为进一步提高组件故障预测的准确性,提出了基于优化AR-HMM的故障预测方法。验证结果表明,该方法具有较高的预测置信度。
4.基于时间应力分析的BIT降虚警和故障预测技术的应用与验证
以某直升机航向姿态系统为对象,设计并实现了具有BIT虚警识别能力和关键部件故障预测能力的PHM系统,并通过理论分析和试验对本文所研究的虚警识别方法和故障预测方法进行了验证。结果表明,本文所研究方法具有较好的虚警识别和故障预测效果。
The high False Alarm Rate (FAR) in Built-in Test (BIT) and low fault prognostics ability of equipments are two common key problems in the fault diagnosis field. They directly prevent BIT and fault prognostics system from more extensive application. In recent years, with some new conceptions such as Prognostics and Health Management (PHM) appearing, how to reduce FAR of BIT and improve the fault prognostics ability become more and more important.
The time stress include all kinds of environment and operating stress such as shock, vibration, temperature and so on that the equipment suffered in the manufacture, transport and operating process. On the one hand, the real-time time stress is the main factor that disables system temporarily and incurs the BIT false alarms. On the other hand, the time stress directly induce degradation or deviation of the components from an expected normal condition. Therefore, analyzing the time stress can reduce false alarms of the BIT and improve fault prognostics ability effectively.
This paper is aiming to solve the two problems of BIT's high FAR and equipment's low fault prognostics ability in fault diagnosis field. The mechanisms of the faults and BIT false alarms induced by time stress are analyzed systematically. Then, the BIT false alarm reducing and fault prognostics technologies based on time stress analysis are studied.
The main contents of the dissertation are as follows.
1. The mechanisms of how the time stress induces faults and BIT false alarms are analyzed systematically and some relative models are built.
Firstly, the mechanisms of the faults and BIT false alarms induced by time stress are analyzed in detail. In order to analyze the relationship between time stress and BIT fault alarm, a relevance analysis model based on Support Vector Machine (SVM) is built. The model can acquire the main time stress region that induces the BIT false alarms. Secondly, a dynamic description damage model is researched based on Generalized Stochastic Petri Nets (GSPN). This model can analyze the evolution relationship between time stress and component's damage. Finally, a probability fault evolution model based on Hidden Markov Model (HMM) is studied. This model can analyze the evolution process of component's fault.
2. The BIT false alarm reducing technology based on time stress analysis is studied.
(1) Two relevance analysis methods between time stress and BIT false alarm based on Failure Mode, Effect and Stress Analysis (FMESA) and logistic regression are studied. These two methods can analyze the main stress factor that induce BIT false alarm from qualitative and quantitative aspects. In order to acquire the optimization threshold values of the relevance analysis model between time stress and BIT false alarm, the threshold values optimization selection method is discussed in detail.
(2) A false alarm recognition method based on SVM and time stress analysis is studied, and it can recognise the false alarms in the time stress relevance region. But this method can not recognise the real faults in the time stress relevance region and the false alarms out of the time stress relevance region effectively. In order to solve the first problem, a false alarm recognition method based on SVM-HMM and time stress analysis is proposed. In order to solve the second problem, a false alarm recognition method based on Kernel based Principal Component Clustering Analysis (KPCCA) and HMM is studied. Finally, a synthetical false alarm recognition method based on SVM-KPCCA-HMM and time stress analysis is proposed. And it can recognise the false alarms and real faults in the whole time stress region.
3. The fault prognostics technology based on time stress analysis is studied.
(1) An accumulative damage assessment method based on component's failure models and Multi-component Life Consumption Monitoring (MLCM) is studied. Then, the accumulative damage assessment methods of joint and separate components are studied in detail. It can transform the time stress suffered in the life time into equipment's damage.
(2) Considering the history current of damage and the abrupt change affection of time stress, a fault prognostics method based on dynamic optimization auto-regression (AR) model and multipoint damages is studied. Because there are some uncertainties in the damage assessment process, a fault prognostics method based on optimization AR model and HMM is proposed. This method can improve the accuracy. The experimental results show that this method has a higher degree of confidence.
4. The BIT false alarm reducing and fault prognostics technologies based on time stress are applied and validated.
In order to evaluate the efficiency of the BIT false alarm reducing and fault prognostics technologies, a PHM system for heading attitude system of the helicopter is built and implemented. Then, some time stress experimental studies on the heading attitude system are executed. The results show that BIT false alarm reducing technology in this paper can reduce false alarms of BIT effectively, and the fault prognostics technology has a good fault prognostics effect.
引文
[1]Hess A, Calvello G, Dabney T. PHM a key enabler for the JSF autonomic logistics support concept.2004 Aerospace Conference,2004 IEEE Proceedings, Vol.6:3543-3550.
[2]Henley S, Currer R, Hess A. Autonomic logistics -the support concept for the 21st century.2000 Aerospace Conference,2000 IEEE Proceedings, Vol.6: 417-421.
[3]Byer B, Hess A, Fila L. Writing a convincing costbenefit analysis to substantiate autonomic logistics.2001 Aerospace Conference,2001 IEEE Proceedings, Vol.6: 3095-3013.
[4]张宝珍.21世纪的保障方案-JSF的自主式后勤.航空维修与工程,2003.1：27-30.
[5]Hess A, Fila L. The joint strike fighter(JSF) PHM concept:potential impact on aging aircraft problems.2002 Aerospace Conference Proceedings,2002 IEEE Proceedings, Vol.6:3021-3026.
[6]Smeulers M, Zeelen R, Bos A. PROMIS-a generic PHM methodology applied to aircraft subsytems.2002 Aerospace Conference,2002 IEEE Proceedings: 3153-3159.
[7]郑哲敏,赵亚溥.PHM-机械失效的预测和安全管理系统.力学进展,1999.5：243-268.
[8]曾声奎,Pecht M,吴际.故障预测与健康管理(PHM)技术的现状与发展.航空学报,2005.5：626-632.
[9]张宝珍,曾天翔.先进的故障预测与状态管理技术.测控技术,2003.11：4-6.
[10]徐萍,康锐.预测与状态管理系统(PHM)技术研究.测控技术,2004.12：58-60.
[11]Kalgren P W, Byington C S, Roemer M J, et al. Defining PHM-a lexical evolution of maintenance and logistics. Systems Readiness Technology Conference,2006.9:353-358.
[12]Orsagh R F, Brown D W, Kalgren P W, et al. Prognostic health management for avionic systems. Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, USA.2006:1-7.
[13]Smith G, Schroeder J B, Navarro S, et al. Development of a prognostics & health management capability for the joint strike fighter. Autotestcon Proceedings, 1997:676-682.
[14]Massam S. Verification of PHM Capabilities:A joint customer/industrial perspective.2002 Aerospace Conference,2002 IEEE Proceedings:2799-2813.
[15]温熙森,徐永成,易晓山,陈循.智能机内测试理论与应用.北京：国防工业出版社,2002.
[16]钱彦岭.测试性建模技术及其应用研究[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2002.
[17]田仲,石君友.系统测试性设计分析与验证.北京：北京航空航天大学出版社,2003.
[18]曾天翔.电子设备测试性及诊断技术.北京：航空工业出版社,1996.
[19]甘茂治.维修性设计与验证.北京：国防工业出版社,1995.
[20]陈光禹.可测性设计技术,北京：电子工业出版社,1997.
[2l]张宝珍,曾天翔.智能BIT技术.测控技术,2000.11：1-4.
[22]Robert X G, Abhijit S. Diagnosis from within the system. IEEE Instrumentation & Measurement Magazine,2002.9:43-47.
[23]George L D. Two-level maintenance:how do you get there. Proceedings Annual Reliability and Maintainability Symposium,1991:397-399.
[24]Merlino D H, Hadjilogiou J. Built-in test strategies for military systems. Reliability and Maintainability Symposium,1989:59-65.
[25]刘冠军.基于边界扫描的智能板级BIT技术研究[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2000.
[26]杨光.机电产品BIT系统传感层降虚警的理论与技术研究[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2004.
[27]柳新民.机电系统BIT间歇故障虚警抑制技术研究[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2005.
[28]王新峰.机电系统BIT特征层降虚警技术研究.[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2005.
[29]姜云春.基于模型的控制系统鲁棒故障诊断技术研究.[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2006.
[30]Mishra S, Ganesan S, Pecht M, and Xie J. Life consumption monitoring for electronics prognostics.2004 Aerospace Conference,2004 IEEE Proceedings, Big Sky, MT,2004.3,Vol.5:3455-3467.
[31]Pecht M, Dube M, Natishan M, et al. An evaluation of built-in test. IEEE Transactions on Aerospace and Electronic Systems,2001,Vol.37.1:266-272.
[32]Ramakrishnan A, Pecht M. A life consumption monitoring methodology for electronic systems. IEEE Transactions on Components and Packaging Technologies,2003.9,Vol.26.3:625-634.
[33]Valentin R, Osterman M, Newman B. Remaining life assessment of aging electronics in avionic applications.2003 Reliability and Maintainability Symposium,2003.1:313-318.
[34]Vichare N, Pecht M. Prognostics and health management of electronics. IEEE Transactions on Packaging and Manufacturing Technology,2006.5:222-229.
[35]Vichare N, Rodgers P, Eveloy V, Pecht M. In situ temperature measurement of a notebook computer-a case study in health and usage monitoring of electronics. IEEE Transactions on Device and Materials Reliability,2004.9:658-663.
[36]Pecht M, Dube M, Natishan M, Williams R. Evaluation of built-in test. IEEE Transactions on Aerospace and Electronic Systems,2001:266-271.
[37]曾天翔.测试性在现代军用飞机中的应用.测控技术,1993.2：2-5.
[38]Dean J S. Integrated diagnostics:confusion and solutions. Autotestcon Proceedings,1996.9:436-440.
[39]Rogin H. Using built-in-test to reduce TPS run times and improve TPS reliability. Autotestcon Proceedings,1999.9:345-351.
[40]杨为民.可靠性、维修性、保障性总论.北京：国防工业出版社,1995.
[41]Irwing H. Built-in test-past mistakes, present problems and future solutions. Reliability Engineering,1986.15:245-261.
[42]Anderson J M, Laskey J M. The enemy is FA, CND, and RTOK, Autotestcon Proceedings,1988:185-189.
[43]Richards D W. Smart BIT:a plan for intelligent built-in test. IEEE Aerospace and Electronics Systems Magazine,1989.1:26-29.
[44]John Z, Ken A. SMART BIT-2:adding intelligence to built-in test. Proc. of NAEC,1989:2035-2042.
[45]Zourides V G. Smart built-in-test (BIT):an overview. Autotestcon Proceedings, 1989:67-74.
[46]Joseph L. Reliability improvement by removing electrical interrupts in equipment, which result in "retest OK", "cannot dulplicate", "no defect found". Proc. of Annual Reliability and Maintainability Symposium,1982:1110-1114.
[47]William R S, John W S. Analysis of false alarm during system design. Autotestcon Proceedings,1992:657-660.
[48]徐永成.BIT中智能故障诊断理论与方法研究[博士学位论文].长沙：国防科技大学机电工程及自动化学院,1999.
[49]曲东才.系统可测试性与航空武器系统.测控技术,1997.2：8-10.
[50]John Z. Smart BIT/TSMD integrate. AD-A247192, USA:Rome laboratory, 1991.
[51]Malcolm J G. BIT false alarm:an important factor in operational readiness. Proc. of annual reliability and maintainability symposium,1982:206-212.
[52]Aylstock F, Elerin L, Hintz J, Learoyd C. Neural network false alarm filter. NTIS No:AD-A293114/5/HDM.
[53]Aylstock F, Elerin L, Hintz J. Neural network false alarm filter, AD-A293097, USA:Raytheon Company,1994.
[54]张宝珍,曾天翔.改善电子系统故障诊断能力的神经网络虚警过滤器技术.航空标准化与质量,2001.3：43-47.
[55]温熙森,徐永成,胡茑庆.采用AI理论降低电子设备BIT虚警率.测控技术,1998.4：31-33.
[56]Wang X F, Qiu J, Liu G J, Qian Y L. Research on the methods of decreasing false alarms for mechantronic equipments BIT. Proceedings of the International Symposium on Test and Measurement,2003.4:3347-3350.
[57]杨光,邱静,温熙森,刘冠军.机内测试系统虚警问题的数学模型分析.西南交通大学学报,2003.5：581-583.
[58]张雨,温熙森.基于灰色关联度和模糊贴近度的复杂设备瞬态虚警辨识.国防科技大学学报,1998.5：45-50.
[59]Mishra A, Banerjee P. An algorithm-based error detection scheme for the multigrid method. IEEE Transactions on Computers,2003.9:1089-1099.
[60]Yen G G, Liang W H. Online multiple-model-based fault diagnosis and accommodation. IEEE Transactions on Industrial Electronics,2003.2:296-312.
[61]Sheppard J W. Accounting for false indication in a Bayesian diagnostics framework. Autotestcon Proceedings,2003:273-279.
[62]Pirinen T, Pertila P, Visa A. Toward intelligent sensors-reliability for time delay based direction of arrival estimates. IEEE International Conference on Acoustics, Speech, and Signal Processing,2003.5:197-200.
[63]Fenger M, Lloyd B A, Thomson W T. Development of a tool to detect faults in induction motors via current signature analysis. IEEE-IAS/PCA,2003:37-46.
[64]Malcolm J G. BIT false alarm:an important factor in operational readiness. Proc. of Annual Reliability and Maintainability Symposium,1982:206-212.
[65]John Zm Ken A. Smart BIT-2:adding intelligence to built-in test. Proc. of NAEC.1989:2035-2042.
[66]Richards W. Smart built-in test. N90-25512,1990:123-126.
[67]Hess A, Fila L. The joint strike fighter (JSF) PHM concept:potential impact on aging aircraft problems.2002 Aerospace conference,2002, IEEE proceedings, vol.6:3021-3026.
[68]Gill, J J. Lessons learned from rotary-and fixed-wing HUMS applications.2000 Aerospace Conference,2000 IEEE Proceedings, Vol.6:423-431.
[69]Byington C S, Watson M, Roemer M J, Galic T R, McGroarty J J. Prognostic enhancements to gas turbine diagnostic systems.2003 Aerospace Conference, 2003 IEEE Proceedings,Vol.7:3247-3255.
[70]Byington C S, Roemer M J, Galie T. Prognostic enhancements to diagnostic systems for improved condition-based maintenance.2002 Aerospace Conference 2002 IEEE Proceedings, Vol.6:2815-2824.
[71]Kacprzynski G J, Roemer M J, Hess A J. Health management system design. development, simulation and cost/benefit optimiza-tion. Aerospace Conference Proceedings.2002.06:3065-3072.
[72]Williams J H, Davies A, Drake P R. Condition-based maintenance and machine diagnostics. Chapman & Hall,1994.
[73]Alford L D. The problem with aviation COTS. Aerospace and Electronic Systems Magazine,2001.2, Vol.16:33-37.
[74]Gartner D L, Dibbert S E. Application of integrated diagnostic process to non-avionics systems. Autotestcon Proceedings,2001:229-238.
[75]http://www.ridgetop-group.com/docs/News_Release_C&D_Tech.pdf.
[76]http://www.calce.umd.edu/whats new/upcoming/2004/workshop/07.pdf.
[77]Kirkland L V, Pombo T, Nelson K, Berghout F. Avionics health management: searching for the prognostics grail.2004 Aerospace Conference,2004 IEEE Proceedings, Vol 5:3448-3454.
[78]Williams R, Banner J, Knowles I, Natishan M, Pecht M. An investigation of 'cannot duplicate' failure. Quality and Reliability Engineering International, 1998,Vol.14:331-337.
[79]Pecht M, Dube M, Natishan M, Williams R, Banner J, Knowles I. An evaluation of built-in test. IEEE Transactions on Aerospace and Electronic Systems,2001.1, Vol.37.1:266-272.
[80]Pecht M, Ramappan V. Are components still themajor problem:a review of electronic system and device field failure returns. IEEE Transactions on Hybrids, and Manufacturing Technology,1992.9, Vol.15:1160-1164.
[81]Williams J H, Davies A, Drake P R. Condition-based maintenance and machine diagnostics, Chapman & Hall,1994.
[82]Natke H G, Cempel C. Model-aided diagnosis of mechanical systems: fundamentals, detection, localization, and assessment. Springer Verlag,1997.
[83]Ohring M. Reliability and failure of electronic materials and devices. Academic Press,1998.
[84]http://www.calce.umd.edu/whats new/upcoming/2004/workshop/06.pdf.
[85]Sandborn P. A decision support model for determining the applicability of prognostic health management (PHM) approaches to electronic systems. Reliability and Maintainability Symposium,2005.1:422-427.
[86]http://www.calce.umd.edu/whats new/upcoming/2004/workshop/09.pdf.
[87]http://www.calce.umd.edu/whats new/upcoming/2004/workshop/05.pdf.
[88]http://www.calce.umd.edu/whats_new/upcoming/2004/workshop/03.pdf.
[89]Kacprzynski G J, Roemer M J, Modgil G, Palladino A, Maynard K. Enhancement of physics-of-failure prognostic models with system level features., 2002 Aerospace Conference,2002 IEEE Proceedings, Vol.6:2919-2925.
[90]Havey G, Louis S, Bruska S. Micro-time stress measurement device development. Rome Laboratory Air Force Materiel Command Griffiss Air Force Base, New York.1995.
[91]Stuart P, Broadwater, Tod A, Oblak, et al. Miantenance processor/time stress measurement device (MP/TSMD) use for failure trend analysis. Annual Reliability and Maintainability Symposium,1999:228-238.
[92]Vincent R, Bruno F. Development and use of a miniaturized health monitoring device.42nd Annual International Reliability Physics Symposium,2004: 645-646.
[93]Richards D W, Collins J A. Intelligent built-in test and stress management. AUTOTESTCON'89. IEEE Automatic Testing Conference,1989:261-266.
[94]Skormin V A, Popyack L J. Reliability of flight critical system components and their "history of abuse". Aerospace and Electronics Conference,1995, vol.1: 376-381.
[95]Broadwater S P, Cockey E A. Time stress measurement device use for on-board diagnostic support. Autotestcon Proceedings,1993:251-258.
[96]Swart P W. RTOK elimination with TSMM. Proceedings of the IEEE 1991 National Aerospace and Electronics Conference,1991.5,vol.l:214-220.
[97]张宝珍.时间应力测量装置技术(一种有发展前途的军民两用技术).2001年中国航空学会可靠性工程学术年会论文集,中国航空学会,2001：145-149.
[98]Nickerson B, Lally R. Development of a smart wireless networkable sensor for aircraft engine health management.2001 Aerospace Conference,2001 IEEE Proceedings,Vol 7:3255-3262.
[99]Deliverable report 16 of the environmental life cycle information management and acquisition for consumer products. Project Funded By the European Community under the'Competitive and Sustainable Growth Programme', 2005.5.
[100]Mishra S, Pecht M, Smith T, McNee I, Harris R. Remaining life prediction of electronic products using life consumption monitoring approach. European Microelectronics Packaging and Interconnection Symposium Cracow, Poland, June 16-18,2002:136-142.
[101]Mishra S, Pecht M. In-situ sensors for product reliability monitoring. proceedings of SPIE,2002:10-19.
[102]Pecht M, Dube M, Natishan M, Knowles I. An evaluation of built-in test. IEEE Transactions onAerospace and Electronic Systems,2001.1,Vol.37.1:266-272.
[103]Ramakrishnan A, Pecht M. Load characterization during transportation. Microelectronics Reliability,2004.1,Vol.44.2:333-338.
[104]Middendorf A, Griese H, Grimm W, Reichl H. Embedded life-cycle information module for monitoring and identification of product use conditions.3rd International Symposium on Environmentally Conscious Design and Inverse Manufacturing,2003.12:733-740.
[105]Ginart A, Barlas I, Goldin J, Dorrity J. Automated feature selection for embeddable prognostic and health monitoring (PHM) architectures. Systems Readiness Technology Conference,2006.9:195-201.
[106]Brotherton T, Luppold R, Padykula P, Wade R. Generic integrated PHM/ controller. IEEE System Aerospace Conference,2005.3:1-11.
[107]胡峰,孙国基.过程监控技术及其应用.北京：国防工业出版社,2001：34-47.
[108]Basseville M, Nikiforov I V. Detection of abrupt changes-theory and aplication. Prentice-Hall Englewood Cliffs,1993.
[109]周东华,孙优贤.控制系统的故障检测与辨识技术研究.北京：清华大学出版社,1994.
[110]Sorensen B A, Kelly G, Sajecki A. An analyzer for detecting intermittent faults in electronic devices. Autotestcon Proceedings,1994.9:417-421.
[111]Brent S, Paul S, Gary K, Artur S. Analyzer for detecting aging faults in electronic devices. Autotestcon Proceedings,1994.9:323-329.
[112]Roberts M W. A fault-tolerant scheme that copes with intermittent and transient faults in sequential circuits. Proceedings of the 32nd Midwest Symposium on Circuits and Systems,1989.1:36-39.
[113]赵兴运.机载电子产品的系统性失效问题分析.电子产品可靠性与环境试验,2002.1：28-31.
[114]Correcher A, Garcia E, Morant F, Quiles E. Intermittent failure diagnosis in industrial processes.2003 IEEE International Symposium on Industrial Electronics,2003.6, Vol2:723-728.
[115]章新瑞,任占勇.可靠性试验中环境应力与产品故障机理间关系研究.环境技术,2000.5：7-11.
[116]邓永孝.半导体器件失效分析.北京：宇航出版社,1991.
[117]夏泓.电子元器件失效分析及应用.北京：国防工业出版社,1998.
[118]许凤和.航空非金属件失效分析.北京：科学出版社,1993.
[119]章黎明,叶迪镇.电子设备组件环境应力筛选分析-潜在缺陷、故障模式和失效机理.电子产品可靠性与环境试验,1997.6：28-31.
[120]Constantinescu C. Impact of deep submicron technology on dependability of VLSI circuits. IEEE Proceedings of the International Conference on Dependable Systems and Networks,2002:205-209.
[121]蒋瑜,杨雪,阮启明.机械设备故障规律及运行趋势预测方法综述.机电一体化,2001.3：14-17.
[122]吴今培,肖键华.智能故障诊断与专家系统.北京：科学出版社,1997.
[123][日]盐见弘,杨家坚,关成勋(译).失效物理基础.北京：科学出版社,1982.
[124]王国胜,钟义信.支持向量机的若干新进展.电子学报,2001.10：1397-1400.
[125]张学工.关于统计学习理论与支持向量机.自动化学报,2000.1：32-42.
[126]Hao Y, Sun J G, Yang G Q, et al. The application of support vector machines to gas turbine performance diagnosis. Chinese Journal of Aeronautics,2005.1: 15-19.
[127]张周锁,李凌均,何正嘉.基于支持向量机故障分类器的参数优化研究.西安交通大学学报,2003.11：1101-1109.
[128]Salat R, Osowski S. Accurate fault location in the power transmission line using support vector machine approach. IEEE Transactions on Power Systems,2004.2: 979-986.
[129]Gao J F, Shi W G, Tan J X. Support vector machines based approach for fault diagnosis of valves in reciprocating pumps. IEEE CCECE 2002. Conference on Electrical and Computer Engineering, Canadian May 2002, Vol.3:1622-1627.
[130]Yan W W, Shao H H. Application of support vector machine nonlinear classifier to fault diagnoses. IEEE Proceedings of the 4th World Congress on Intelligent Control and Automation,2002.4:2697-2700.
[131]Zhai Y J, Li R X, Wang D F, Han P. Risk function based fuzzy judge SVM algorithm and its application. IEEE International Conference on Systems, Man and Cybernetics,2003.3:2406-2411.
[132]Vapnik V N.统计学习理论的本质.张学工译.北京：清华大学出版社,2000.
[133]倪侃.随机疲劳累积损伤理论研究进展.力学进展,1999.2：43-65.
[134]赵少汴.变幅载荷下的有限寿命疲劳设计方法和设计数据.机械设计,2000.1：5-8.
[135]周传月,郑红霞,罗慧强.MSC.Fatigue疲劳分析应用于实例.北京：科学出版社,2005.3.
[136]林创.随机Petri网和系统性能评价.北京：清华大学出版社,2005.4.
[137]孙俊清,王鹏涛,于长云.随机Petri网对可修复系统的建模和分析.天津大学学报,2004.3：258-263.
[138]谢锦辉.隐Markov模型(HMM)及其在语音处理中应用.华中理工大学出版社,1995.
[139]易克初.语音信号处理.北京：国防工业出版社,2000.
[140]Lawrance R, Rabiner. A. Tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE,1989.2:257-286.
[141]张杰,黄志同,王晓兰.语音识别中隐马尔可夫模型状态数的选取原则及研究.计算机工程与应用,2000.1：67-70.
[142]张焱,张杰,黄志同.语音识别中隐马尔可夫模型状态数的研究.南京理工大学学报,1998.3：208-212.
[143]张锡英,李光琪,张艳秋.隐马尔柯夫模型在话者识别中的应用.佳木斯大学学报(自然科学版),2000.1：53-55.
[144]Atlas L, Ostendorf M, Bernard G D. Hidden Markov models for monitoring machining tool-wear. IEEE International Conference on Acoustics, Speech, and Signal Processing,2000.6:3887-3890.
[145]Hatzipantelis E, Murray A, Penman J. Comparing hidden Markov models with artificial neural network architectures for condition monitoring applications. Fourth International Conference on Artificial Neural Networks,1995.6: 369-374.
[146]Jaehak C, Powers E J, Grady W M, Bhatt S C. Power disturbance classifier using a rule-based method and wavelet packet-based hidden Markov model. IEEE Transactions on Power Delivery,2002.1:233-241.
[147]Heck L P, McClellan J H. Mechanical System Monitoring using HMMs. IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.3, 1991:1697-1700.
[148]Assouad P E, Liu J B, Pasek Z J. Performance assessment of two classification schemes for cutting tool degradation monitoring. IEEE International Conference on Systems, Man and Cybernetics,2003,4:3769-3774.
[149]Zhang D F, Zeng Y, Zhou X J, Cheng Y D. The pattern recognition of non-destructive testing based on HMM. Proceedings of the 4th World Congress on Intelligent Control and Automation,2002,3:2198-2202.
[150]蒋仁言,左明健.可靠性模型与应用.北京：机械工业出版社,1999.
[151]程侃.寿命分布类与可靠性数学理论.北京：科学出版社,1999.
[152]曾声奎,赵廷弟,张建国,康锐,石君友.系统可靠性设计分析教程.北京：北京航空航天大学出版社,2001.
[153]曾天翔,杨先振,王维翰.可靠性及维修性工程手册(上册).北京：国防工业出版社,1994.
[154]Suresh S.材料的疲劳.北京：国防工业出版社,1999.
[155]张行,赵军.金属构件应用疲劳损伤力学.北京：国防工业出版社,1998.
[156]王莲芬,许树柏.层次分析法引论.北京：中国人民大学出版社,1990.6.
[157]梁劲松.军用直升机可靠性的综合化发展.航空科学技术,2005.5：6-9.
[158]孙东红.直升机振动环境与机载设备振动环境试验若干问题的探讨.航空标准化与质量,2004.4：46-49.
[159]Skormin V A, Popyack L J. Reliability of flight critical system components and their "history of abuse". Aerospace and Electronics Conference,1995,vol.1: 376-381.
[160]何秀丽,刘次华.logistic回归中的加权最小二乘估计.应用数学,2004.17：144-147.
[161]Nakamichi R, Imoto S, Miyano S. Case-control study of binary disease trait considering interactions between SNPs and environmental effects using logistic regression. Proceedings of the Fourth IEEE Symposium on Bioinformatics and Bioengineering,2004:73-78.
[162]洪楠,侯军.SAS统计分析系统教程新编.北京：清华大学出版社,2004.
[163]肖健华,樊可清,吴今培,杨叔子.应用于故障诊断的SVM理论研究.振动、测试与诊断,2001.4：258-262.
[164]Nello C, John S T.支持向量机导论.李国正,王猛,曾华军[译].北京：电子工业出版社,2004.
[165]刘志刚,李德仁,秦前清,史文中.支持向量机在多分类问题中的推广.计算机工程与应用,2004.7：10-13.
[166]张周锁,李凌均,何正嘉.基于支持向量机的多故障分类器及应用.机械科学与技术,2004.5：536-539.
[167]李凌均,张周锁,何正嘉.支持向量机在机械故障诊断中的应用研究.计算机工程与应用,2002.9：19-21.
[168]Aylstock F. Neural network false alarm filter. ADA293097. RADC,1995.
[169]王旭,王宏,王文辉.人工神经元网络原理与应用.沈阳：东北大学出版社,2000.12：19-65.
[170]王伟.人工神经网络原理.北京：北京航空航天大学出版社,1995.10：15-132.
[171]John S T, Nello C.模式分析的核方法.赵玲玲等[译].北京：机械工业出版社,2006：89-120.
[172]Theodoridis S, Koutroumbas K.模式识别.李晶皎等,译.北京：电子工业出版社,2006：425-470.
[173]Scholkopf B, Smola A J, Muller K R. Kernel principal component analysis, advances in kernel methods-support vector learning. MIT Press, Cambridge, MA,1999:327-352.
[174]John S T, Nello C. Kernel method for pattern analysis, Cambridge University Press,2004.
[175]David V, Sanchez A. Advanced support vector machines and kernel methods. Neurocomputing,2003:5-20.
[176]褚卫华.模块级电子产品可靠性强化试验方法研究[博士学位论文].长沙：国 防科学技术大学,2003.
[177]王考.基于PQFP的试验剖面优化与缺陷激发效果分析[硕士学位论文].国防科学技术大学,2003.
[178]Yao Q Z, Qu J M, Wu S X. Estimate The thermomechanical life at fatigue of two chip scale packages.1999 IEEE:Electronic Components and Technology Conference,1999.6:797-802.
[179]Neal F E, Thomas J K, Scott A S, Jon R L. Mechanical behaviors of 60/40 tin-lead solder lap joints. IEEE Transactions on components hybrids and manufacturing technology, Vol.12, NO.4,1998.12.
[180]Darveaux R. Effect of simulation methodology on solder joint crack growth correl-ations. Proceedings of 50# Electronic Components & Technology Conference,2000.5:1048-1058.
[181]Wong T E, Reed B A, Cohe H M, Chu D W. Development of BGA solder joint vibration fatigue life prediction model.1999 IEEE:Electronic Components and Technol-ogy Conference,1999.6:149-154.
[182]庄奕琪.微电子器件应用可靠性技术.北京：电子工业出版社,1996.
[183]Doyle B S, Mistry K R. A lifetime prediction method for hot-carrier degradation in surface-channel p-MOS devices. IEEE Trans. Electron Devices 1990.5: 1301-1307.
[184]Hu C, Tam S C, Hsu F C, et al. Hot-electron-induced MOSFET degradation. IEEE Trans. Electron Devices,1985.2:375-384.
[185]Renn S H, et al. Hot carrier effects and reliable lifetime prediction in deep submicron N-and P-Channel SO IMOSFET's. IEEE Trans, Electron Devices, 1998.11:2335-2342.
[186]Son K I, Soma M. Dynamic life-estimation of CMOS ICs in real operating environment:precise electrical method and MLE. IEEE Transactions on reliability,1997.3, vol.46:31-37.
[187]HU C, Tam S C, Hsu F C, et al. Hot-electron induced MOSFET degradation% model, monitor, and improvement. IEEE Trans. Electron Devices,1985.2,Vol.32: 375-385.
[188]Chen I C, Holland S, Hu C. A quantitive physical model for time dependent breakdown in SiO2. IEEE Int Reliab Phys Symp, Orlando, Florida,1985:24-31.
[189]Kaczer B, Degraeve R, Rasras M,et al. Impact of MOSFET gate oxide breakdown on digital circuit operation and reliability. IEEE Trans. Electron Devices,2002:500-506.
[190]MePherson J W, Khamankar R B. Molecular model for intrinsic time-dependent dielectric breakdown in SiO2 dielectrics and the reliability implications for hyper-thin gate oxide. Semicond Sci Technol,2000:462-469.
[191]Montoya J, Levit L, Englisch A. A study of the mechanisms for ESD damage to reticles. Electrical Overstress/Electrostatic Discharge Symposium Proceedings, 2000.9:394-405.
[192]高光渤,李学信.半导体器件可靠性性物理.北京：科学出版社,1987.11：436-490.
[193]季馨.电子设备振动分析与试验.南京：东南大学出版社,1992.10：4-20.
[194]周德俭,潘开林,吴兆华.基于最小能量原理的SMT焊点三维形态预测.电子学报,1999.5：2-3.
[195]朱奇农,王国忠,罗乐.倒装焊中复合SnPb焊点形态模拟.电子学报,2000.5：1-3.
[196]王国忠,王青春,邵琳,钱乙余.片式元件SMT焊点三维形态计算与试验测量.电子工艺技术,1996.5：2-3.
[197]周德俭,潘开林,刘常康.SMT焊点形态成形和焊点可靠性CAD.半导体学报,2002.2：1-4.
[198]张磊,李明雨,王青春.细间距器件焊点桥接研究.电子工艺技术,2001.1：2-3.
[199]黄春跃,周德俭.细微间距器件焊点形态成形建模与预测.电子工艺技术,2000.4：1-4.
[200]Ira R H, James M P, Timothy J S. Effect of solder joint geometry predi-cted fatigue life of BGA solder joints.1996 IEEE Intersociety Conference on Thermal Phenomena,1996:187-184.
[201]Bret A, Zahn. Finite element based solder joint fatigue life predictions for a same die stacked chip scale ball grid array Package.2002 IEEE:International Electronics Manufac-turing Technology Symposium,2002:274-285.
[202]Zhang L L, Chee S S, Maheshwari A, Funcell A. Experimental and finite element analysis of cavity down BGA package solder joint reliability.2000 IEEE:Electr-onics Packaging Technology Conference,2000:391-397.
[203]Andrew M. MOTOLOLA semiconductor technical data:plastic ball grid array(PBGA), MOTOROLA, Inc.1996:1-19.
[204]徐维新.电子设备可靠性热设计指南.北京：电子工业出版社,1995.
[205][美]Suresh S.材料的疲劳(第2版).王光中等[译].北京：国防工业出版社,1999.
[206]张永昌.MSC.Nastran有限元分析理论基础与应用.北京：科学出版社,2004.
[207]温熙森,陈循,唐丙阳.机械系统动态分析理论与应用.长沙：国防科技大学出版社,1997.
[208]金星,洪延姬.工程系统可靠性数值分析方法,北京：国防工业出版社,2002：42-48.
[209]Liao H T, Zhao W B, Guo H R. Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model. Reliability and Maintainability Symposium,2006.1:127-132.
[210]Sathyanarayan G. System level approach for life consumption monitoring of electronics. Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Master of Science,2004.
[211]贺前华,陆以勤,韦岗.一种新的HMM训练方法.电子学报,2000.9：55-57.
[212]曾声奎,赵延弟,张建国.系统可靠性设计分析教程.北京：北京航空航天大学出版社.2000.5：12-135.
[213]何国伟,戴慈庄.可靠性试验技术.北京：国防工业出版社,1995.
[214]胡志强,法庆衍,洪宝林,张越,徐殷.随机振动试验应用技术.北京：中国计量出版社,1996.12.
[2]Henley S, Currer R, Hess A. Autonomic logistics -the support concept for the 21st century.2000 Aerospace Conference,2000 IEEE Proceedings, Vol.6: 417-421.
[3]Byer B, Hess A, Fila L. Writing a convincing costbenefit analysis to substantiate autonomic logistics.2001 Aerospace Conference,2001 IEEE Proceedings, Vol.6: 3095-3013.
[4]张宝珍.21世纪的保障方案-JSF的自主式后勤.航空维修与工程,2003.1：27-30.
[5]Hess A, Fila L. The joint strike fighter(JSF) PHM concept:potential impact on aging aircraft problems.2002 Aerospace Conference Proceedings,2002 IEEE Proceedings, Vol.6:3021-3026.
[6]Smeulers M, Zeelen R, Bos A. PROMIS-a generic PHM methodology applied to aircraft subsytems.2002 Aerospace Conference,2002 IEEE Proceedings: 3153-3159.
[7]郑哲敏,赵亚溥.PHM-机械失效的预测和安全管理系统.力学进展,1999.5：243-268.
[8]曾声奎,Pecht M,吴际.故障预测与健康管理(PHM)技术的现状与发展.航空学报,2005.5：626-632.
[9]张宝珍,曾天翔.先进的故障预测与状态管理技术.测控技术,2003.11：4-6.
[10]徐萍,康锐.预测与状态管理系统(PHM)技术研究.测控技术,2004.12：58-60.
[11]Kalgren P W, Byington C S, Roemer M J, et al. Defining PHM-a lexical evolution of maintenance and logistics. Systems Readiness Technology Conference,2006.9:353-358.
[12]Orsagh R F, Brown D W, Kalgren P W, et al. Prognostic health management for avionic systems. Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, USA.2006:1-7.
[13]Smith G, Schroeder J B, Navarro S, et al. Development of a prognostics & health management capability for the joint strike fighter. Autotestcon Proceedings, 1997:676-682.
[14]Massam S. Verification of PHM Capabilities:A joint customer/industrial perspective.2002 Aerospace Conference,2002 IEEE Proceedings:2799-2813.
[15]温熙森,徐永成,易晓山,陈循.智能机内测试理论与应用.北京：国防工业出版社,2002.
[16]钱彦岭.测试性建模技术及其应用研究[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2002.
[17]田仲,石君友.系统测试性设计分析与验证.北京：北京航空航天大学出版社,2003.
[18]曾天翔.电子设备测试性及诊断技术.北京：航空工业出版社,1996.
[19]甘茂治.维修性设计与验证.北京：国防工业出版社,1995.
[20]陈光禹.可测性设计技术,北京：电子工业出版社,1997.
[2l]张宝珍,曾天翔.智能BIT技术.测控技术,2000.11：1-4.
[22]Robert X G, Abhijit S. Diagnosis from within the system. IEEE Instrumentation & Measurement Magazine,2002.9:43-47.
[23]George L D. Two-level maintenance:how do you get there. Proceedings Annual Reliability and Maintainability Symposium,1991:397-399.
[24]Merlino D H, Hadjilogiou J. Built-in test strategies for military systems. Reliability and Maintainability Symposium,1989:59-65.
[25]刘冠军.基于边界扫描的智能板级BIT技术研究[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2000.
[26]杨光.机电产品BIT系统传感层降虚警的理论与技术研究[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2004.
[27]柳新民.机电系统BIT间歇故障虚警抑制技术研究[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2005.
[28]王新峰.机电系统BIT特征层降虚警技术研究.[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2005.
[29]姜云春.基于模型的控制系统鲁棒故障诊断技术研究.[博士学位论文].长沙：国防科技大学机电工程及自动化学院,2006.
[30]Mishra S, Ganesan S, Pecht M, and Xie J. Life consumption monitoring for electronics prognostics.2004 Aerospace Conference,2004 IEEE Proceedings, Big Sky, MT,2004.3,Vol.5:3455-3467.
[31]Pecht M, Dube M, Natishan M, et al. An evaluation of built-in test. IEEE Transactions on Aerospace and Electronic Systems,2001,Vol.37.1:266-272.
[32]Ramakrishnan A, Pecht M. A life consumption monitoring methodology for electronic systems. IEEE Transactions on Components and Packaging Technologies,2003.9,Vol.26.3:625-634.
[33]Valentin R, Osterman M, Newman B. Remaining life assessment of aging electronics in avionic applications.2003 Reliability and Maintainability Symposium,2003.1:313-318.
[34]Vichare N, Pecht M. Prognostics and health management of electronics. IEEE Transactions on Packaging and Manufacturing Technology,2006.5:222-229.
[35]Vichare N, Rodgers P, Eveloy V, Pecht M. In situ temperature measurement of a notebook computer-a case study in health and usage monitoring of electronics. IEEE Transactions on Device and Materials Reliability,2004.9:658-663.
[36]Pecht M, Dube M, Natishan M, Williams R. Evaluation of built-in test. IEEE Transactions on Aerospace and Electronic Systems,2001:266-271.
[37]曾天翔.测试性在现代军用飞机中的应用.测控技术,1993.2：2-5.
[38]Dean J S. Integrated diagnostics:confusion and solutions. Autotestcon Proceedings,1996.9:436-440.
[39]Rogin H. Using built-in-test to reduce TPS run times and improve TPS reliability. Autotestcon Proceedings,1999.9:345-351.
[40]杨为民.可靠性、维修性、保障性总论.北京：国防工业出版社,1995.
[41]Irwing H. Built-in test-past mistakes, present problems and future solutions. Reliability Engineering,1986.15:245-261.
[42]Anderson J M, Laskey J M. The enemy is FA, CND, and RTOK, Autotestcon Proceedings,1988:185-189.
[43]Richards D W. Smart BIT:a plan for intelligent built-in test. IEEE Aerospace and Electronics Systems Magazine,1989.1:26-29.
[44]John Z, Ken A. SMART BIT-2:adding intelligence to built-in test. Proc. of NAEC,1989:2035-2042.
[45]Zourides V G. Smart built-in-test (BIT):an overview. Autotestcon Proceedings, 1989:67-74.
[46]Joseph L. Reliability improvement by removing electrical interrupts in equipment, which result in "retest OK", "cannot dulplicate", "no defect found". Proc. of Annual Reliability and Maintainability Symposium,1982:1110-1114.
[47]William R S, John W S. Analysis of false alarm during system design. Autotestcon Proceedings,1992:657-660.
[48]徐永成.BIT中智能故障诊断理论与方法研究[博士学位论文].长沙：国防科技大学机电工程及自动化学院,1999.
[49]曲东才.系统可测试性与航空武器系统.测控技术,1997.2：8-10.
[50]John Z. Smart BIT/TSMD integrate. AD-A247192, USA:Rome laboratory, 1991.
[51]Malcolm J G. BIT false alarm:an important factor in operational readiness. Proc. of annual reliability and maintainability symposium,1982:206-212.
[52]Aylstock F, Elerin L, Hintz J, Learoyd C. Neural network false alarm filter. NTIS No:AD-A293114/5/HDM.
[53]Aylstock F, Elerin L, Hintz J. Neural network false alarm filter, AD-A293097, USA:Raytheon Company,1994.
[54]张宝珍,曾天翔.改善电子系统故障诊断能力的神经网络虚警过滤器技术.航空标准化与质量,2001.3：43-47.
[55]温熙森,徐永成,胡茑庆.采用AI理论降低电子设备BIT虚警率.测控技术,1998.4：31-33.
[56]Wang X F, Qiu J, Liu G J, Qian Y L. Research on the methods of decreasing false alarms for mechantronic equipments BIT. Proceedings of the International Symposium on Test and Measurement,2003.4:3347-3350.
[57]杨光,邱静,温熙森,刘冠军.机内测试系统虚警问题的数学模型分析.西南交通大学学报,2003.5：581-583.
[58]张雨,温熙森.基于灰色关联度和模糊贴近度的复杂设备瞬态虚警辨识.国防科技大学学报,1998.5：45-50.
[59]Mishra A, Banerjee P. An algorithm-based error detection scheme for the multigrid method. IEEE Transactions on Computers,2003.9:1089-1099.
[60]Yen G G, Liang W H. Online multiple-model-based fault diagnosis and accommodation. IEEE Transactions on Industrial Electronics,2003.2:296-312.
[61]Sheppard J W. Accounting for false indication in a Bayesian diagnostics framework. Autotestcon Proceedings,2003:273-279.
[62]Pirinen T, Pertila P, Visa A. Toward intelligent sensors-reliability for time delay based direction of arrival estimates. IEEE International Conference on Acoustics, Speech, and Signal Processing,2003.5:197-200.
[63]Fenger M, Lloyd B A, Thomson W T. Development of a tool to detect faults in induction motors via current signature analysis. IEEE-IAS/PCA,2003:37-46.
[64]Malcolm J G. BIT false alarm:an important factor in operational readiness. Proc. of Annual Reliability and Maintainability Symposium,1982:206-212.
[65]John Zm Ken A. Smart BIT-2:adding intelligence to built-in test. Proc. of NAEC.1989:2035-2042.
[66]Richards W. Smart built-in test. N90-25512,1990:123-126.
[67]Hess A, Fila L. The joint strike fighter (JSF) PHM concept:potential impact on aging aircraft problems.2002 Aerospace conference,2002, IEEE proceedings, vol.6:3021-3026.
[68]Gill, J J. Lessons learned from rotary-and fixed-wing HUMS applications.2000 Aerospace Conference,2000 IEEE Proceedings, Vol.6:423-431.
[69]Byington C S, Watson M, Roemer M J, Galic T R, McGroarty J J. Prognostic enhancements to gas turbine diagnostic systems.2003 Aerospace Conference, 2003 IEEE Proceedings,Vol.7:3247-3255.
[70]Byington C S, Roemer M J, Galie T. Prognostic enhancements to diagnostic systems for improved condition-based maintenance.2002 Aerospace Conference 2002 IEEE Proceedings, Vol.6:2815-2824.
[71]Kacprzynski G J, Roemer M J, Hess A J. Health management system design. development, simulation and cost/benefit optimiza-tion. Aerospace Conference Proceedings.2002.06:3065-3072.
[72]Williams J H, Davies A, Drake P R. Condition-based maintenance and machine diagnostics. Chapman & Hall,1994.
[73]Alford L D. The problem with aviation COTS. Aerospace and Electronic Systems Magazine,2001.2, Vol.16:33-37.
[74]Gartner D L, Dibbert S E. Application of integrated diagnostic process to non-avionics systems. Autotestcon Proceedings,2001:229-238.
[75]http://www.ridgetop-group.com/docs/News_Release_C&D_Tech.pdf.
[76]http://www.calce.umd.edu/whats new/upcoming/2004/workshop/07.pdf.
[77]Kirkland L V, Pombo T, Nelson K, Berghout F. Avionics health management: searching for the prognostics grail.2004 Aerospace Conference,2004 IEEE Proceedings, Vol 5:3448-3454.
[78]Williams R, Banner J, Knowles I, Natishan M, Pecht M. An investigation of 'cannot duplicate' failure. Quality and Reliability Engineering International, 1998,Vol.14:331-337.
[79]Pecht M, Dube M, Natishan M, Williams R, Banner J, Knowles I. An evaluation of built-in test. IEEE Transactions on Aerospace and Electronic Systems,2001.1, Vol.37.1:266-272.
[80]Pecht M, Ramappan V. Are components still themajor problem:a review of electronic system and device field failure returns. IEEE Transactions on Hybrids, and Manufacturing Technology,1992.9, Vol.15:1160-1164.
[81]Williams J H, Davies A, Drake P R. Condition-based maintenance and machine diagnostics, Chapman & Hall,1994.
[82]Natke H G, Cempel C. Model-aided diagnosis of mechanical systems: fundamentals, detection, localization, and assessment. Springer Verlag,1997.
[83]Ohring M. Reliability and failure of electronic materials and devices. Academic Press,1998.
[84]http://www.calce.umd.edu/whats new/upcoming/2004/workshop/06.pdf.
[85]Sandborn P. A decision support model for determining the applicability of prognostic health management (PHM) approaches to electronic systems. Reliability and Maintainability Symposium,2005.1:422-427.
[86]http://www.calce.umd.edu/whats new/upcoming/2004/workshop/09.pdf.
[87]http://www.calce.umd.edu/whats new/upcoming/2004/workshop/05.pdf.
[88]http://www.calce.umd.edu/whats_new/upcoming/2004/workshop/03.pdf.
[89]Kacprzynski G J, Roemer M J, Modgil G, Palladino A, Maynard K. Enhancement of physics-of-failure prognostic models with system level features., 2002 Aerospace Conference,2002 IEEE Proceedings, Vol.6:2919-2925.
[90]Havey G, Louis S, Bruska S. Micro-time stress measurement device development. Rome Laboratory Air Force Materiel Command Griffiss Air Force Base, New York.1995.
[91]Stuart P, Broadwater, Tod A, Oblak, et al. Miantenance processor/time stress measurement device (MP/TSMD) use for failure trend analysis. Annual Reliability and Maintainability Symposium,1999:228-238.
[92]Vincent R, Bruno F. Development and use of a miniaturized health monitoring device.42nd Annual International Reliability Physics Symposium,2004: 645-646.
[93]Richards D W, Collins J A. Intelligent built-in test and stress management. AUTOTESTCON'89. IEEE Automatic Testing Conference,1989:261-266.
[94]Skormin V A, Popyack L J. Reliability of flight critical system components and their "history of abuse". Aerospace and Electronics Conference,1995, vol.1: 376-381.
[95]Broadwater S P, Cockey E A. Time stress measurement device use for on-board diagnostic support. Autotestcon Proceedings,1993:251-258.
[96]Swart P W. RTOK elimination with TSMM. Proceedings of the IEEE 1991 National Aerospace and Electronics Conference,1991.5,vol.l:214-220.
[97]张宝珍.时间应力测量装置技术(一种有发展前途的军民两用技术).2001年中国航空学会可靠性工程学术年会论文集,中国航空学会,2001：145-149.
[98]Nickerson B, Lally R. Development of a smart wireless networkable sensor for aircraft engine health management.2001 Aerospace Conference,2001 IEEE Proceedings,Vol 7:3255-3262.
[99]Deliverable report 16 of the environmental life cycle information management and acquisition for consumer products. Project Funded By the European Community under the'Competitive and Sustainable Growth Programme', 2005.5.
[100]Mishra S, Pecht M, Smith T, McNee I, Harris R. Remaining life prediction of electronic products using life consumption monitoring approach. European Microelectronics Packaging and Interconnection Symposium Cracow, Poland, June 16-18,2002:136-142.
[101]Mishra S, Pecht M. In-situ sensors for product reliability monitoring. proceedings of SPIE,2002:10-19.
[102]Pecht M, Dube M, Natishan M, Knowles I. An evaluation of built-in test. IEEE Transactions onAerospace and Electronic Systems,2001.1,Vol.37.1:266-272.
[103]Ramakrishnan A, Pecht M. Load characterization during transportation. Microelectronics Reliability,2004.1,Vol.44.2:333-338.
[104]Middendorf A, Griese H, Grimm W, Reichl H. Embedded life-cycle information module for monitoring and identification of product use conditions.3rd International Symposium on Environmentally Conscious Design and Inverse Manufacturing,2003.12:733-740.
[105]Ginart A, Barlas I, Goldin J, Dorrity J. Automated feature selection for embeddable prognostic and health monitoring (PHM) architectures. Systems Readiness Technology Conference,2006.9:195-201.
[106]Brotherton T, Luppold R, Padykula P, Wade R. Generic integrated PHM/ controller. IEEE System Aerospace Conference,2005.3:1-11.
[107]胡峰,孙国基.过程监控技术及其应用.北京：国防工业出版社,2001：34-47.
[108]Basseville M, Nikiforov I V. Detection of abrupt changes-theory and aplication. Prentice-Hall Englewood Cliffs,1993.
[109]周东华,孙优贤.控制系统的故障检测与辨识技术研究.北京：清华大学出版社,1994.
[110]Sorensen B A, Kelly G, Sajecki A. An analyzer for detecting intermittent faults in electronic devices. Autotestcon Proceedings,1994.9:417-421.
[111]Brent S, Paul S, Gary K, Artur S. Analyzer for detecting aging faults in electronic devices. Autotestcon Proceedings,1994.9:323-329.
[112]Roberts M W. A fault-tolerant scheme that copes with intermittent and transient faults in sequential circuits. Proceedings of the 32nd Midwest Symposium on Circuits and Systems,1989.1:36-39.
[113]赵兴运.机载电子产品的系统性失效问题分析.电子产品可靠性与环境试验,2002.1：28-31.
[114]Correcher A, Garcia E, Morant F, Quiles E. Intermittent failure diagnosis in industrial processes.2003 IEEE International Symposium on Industrial Electronics,2003.6, Vol2:723-728.
[115]章新瑞,任占勇.可靠性试验中环境应力与产品故障机理间关系研究.环境技术,2000.5：7-11.
[116]邓永孝.半导体器件失效分析.北京：宇航出版社,1991.
[117]夏泓.电子元器件失效分析及应用.北京：国防工业出版社,1998.
[118]许凤和.航空非金属件失效分析.北京：科学出版社,1993.
[119]章黎明,叶迪镇.电子设备组件环境应力筛选分析-潜在缺陷、故障模式和失效机理.电子产品可靠性与环境试验,1997.6：28-31.
[120]Constantinescu C. Impact of deep submicron technology on dependability of VLSI circuits. IEEE Proceedings of the International Conference on Dependable Systems and Networks,2002:205-209.
[121]蒋瑜,杨雪,阮启明.机械设备故障规律及运行趋势预测方法综述.机电一体化,2001.3：14-17.
[122]吴今培,肖键华.智能故障诊断与专家系统.北京：科学出版社,1997.
[123][日]盐见弘,杨家坚,关成勋(译).失效物理基础.北京：科学出版社,1982.
[124]王国胜,钟义信.支持向量机的若干新进展.电子学报,2001.10：1397-1400.
[125]张学工.关于统计学习理论与支持向量机.自动化学报,2000.1：32-42.
[126]Hao Y, Sun J G, Yang G Q, et al. The application of support vector machines to gas turbine performance diagnosis. Chinese Journal of Aeronautics,2005.1: 15-19.
[127]张周锁,李凌均,何正嘉.基于支持向量机故障分类器的参数优化研究.西安交通大学学报,2003.11：1101-1109.
[128]Salat R, Osowski S. Accurate fault location in the power transmission line using support vector machine approach. IEEE Transactions on Power Systems,2004.2: 979-986.
[129]Gao J F, Shi W G, Tan J X. Support vector machines based approach for fault diagnosis of valves in reciprocating pumps. IEEE CCECE 2002. Conference on Electrical and Computer Engineering, Canadian May 2002, Vol.3:1622-1627.
[130]Yan W W, Shao H H. Application of support vector machine nonlinear classifier to fault diagnoses. IEEE Proceedings of the 4th World Congress on Intelligent Control and Automation,2002.4:2697-2700.
[131]Zhai Y J, Li R X, Wang D F, Han P. Risk function based fuzzy judge SVM algorithm and its application. IEEE International Conference on Systems, Man and Cybernetics,2003.3:2406-2411.
[132]Vapnik V N.统计学习理论的本质.张学工译.北京：清华大学出版社,2000.
[133]倪侃.随机疲劳累积损伤理论研究进展.力学进展,1999.2：43-65.
[134]赵少汴.变幅载荷下的有限寿命疲劳设计方法和设计数据.机械设计,2000.1：5-8.
[135]周传月,郑红霞,罗慧强.MSC.Fatigue疲劳分析应用于实例.北京：科学出版社,2005.3.
[136]林创.随机Petri网和系统性能评价.北京：清华大学出版社,2005.4.
[137]孙俊清,王鹏涛,于长云.随机Petri网对可修复系统的建模和分析.天津大学学报,2004.3：258-263.
[138]谢锦辉.隐Markov模型(HMM)及其在语音处理中应用.华中理工大学出版社,1995.
[139]易克初.语音信号处理.北京：国防工业出版社,2000.
[140]Lawrance R, Rabiner. A. Tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE,1989.2:257-286.
[141]张杰,黄志同,王晓兰.语音识别中隐马尔可夫模型状态数的选取原则及研究.计算机工程与应用,2000.1：67-70.
[142]张焱,张杰,黄志同.语音识别中隐马尔可夫模型状态数的研究.南京理工大学学报,1998.3：208-212.
[143]张锡英,李光琪,张艳秋.隐马尔柯夫模型在话者识别中的应用.佳木斯大学学报(自然科学版),2000.1：53-55.
[144]Atlas L, Ostendorf M, Bernard G D. Hidden Markov models for monitoring machining tool-wear. IEEE International Conference on Acoustics, Speech, and Signal Processing,2000.6:3887-3890.
[145]Hatzipantelis E, Murray A, Penman J. Comparing hidden Markov models with artificial neural network architectures for condition monitoring applications. Fourth International Conference on Artificial Neural Networks,1995.6: 369-374.
[146]Jaehak C, Powers E J, Grady W M, Bhatt S C. Power disturbance classifier using a rule-based method and wavelet packet-based hidden Markov model. IEEE Transactions on Power Delivery,2002.1:233-241.
[147]Heck L P, McClellan J H. Mechanical System Monitoring using HMMs. IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.3, 1991:1697-1700.
[148]Assouad P E, Liu J B, Pasek Z J. Performance assessment of two classification schemes for cutting tool degradation monitoring. IEEE International Conference on Systems, Man and Cybernetics,2003,4:3769-3774.
[149]Zhang D F, Zeng Y, Zhou X J, Cheng Y D. The pattern recognition of non-destructive testing based on HMM. Proceedings of the 4th World Congress on Intelligent Control and Automation,2002,3:2198-2202.
[150]蒋仁言,左明健.可靠性模型与应用.北京：机械工业出版社,1999.
[151]程侃.寿命分布类与可靠性数学理论.北京：科学出版社,1999.
[152]曾声奎,赵廷弟,张建国,康锐,石君友.系统可靠性设计分析教程.北京：北京航空航天大学出版社,2001.
[153]曾天翔,杨先振,王维翰.可靠性及维修性工程手册(上册).北京：国防工业出版社,1994.
[154]Suresh S.材料的疲劳.北京：国防工业出版社,1999.
[155]张行,赵军.金属构件应用疲劳损伤力学.北京：国防工业出版社,1998.
[156]王莲芬,许树柏.层次分析法引论.北京：中国人民大学出版社,1990.6.
[157]梁劲松.军用直升机可靠性的综合化发展.航空科学技术,2005.5：6-9.
[158]孙东红.直升机振动环境与机载设备振动环境试验若干问题的探讨.航空标准化与质量,2004.4：46-49.
[159]Skormin V A, Popyack L J. Reliability of flight critical system components and their "history of abuse". Aerospace and Electronics Conference,1995,vol.1: 376-381.
[160]何秀丽,刘次华.logistic回归中的加权最小二乘估计.应用数学,2004.17：144-147.
[161]Nakamichi R, Imoto S, Miyano S. Case-control study of binary disease trait considering interactions between SNPs and environmental effects using logistic regression. Proceedings of the Fourth IEEE Symposium on Bioinformatics and Bioengineering,2004:73-78.
[162]洪楠,侯军.SAS统计分析系统教程新编.北京：清华大学出版社,2004.
[163]肖健华,樊可清,吴今培,杨叔子.应用于故障诊断的SVM理论研究.振动、测试与诊断,2001.4：258-262.
[164]Nello C, John S T.支持向量机导论.李国正,王猛,曾华军[译].北京：电子工业出版社,2004.
[165]刘志刚,李德仁,秦前清,史文中.支持向量机在多分类问题中的推广.计算机工程与应用,2004.7：10-13.
[166]张周锁,李凌均,何正嘉.基于支持向量机的多故障分类器及应用.机械科学与技术,2004.5：536-539.
[167]李凌均,张周锁,何正嘉.支持向量机在机械故障诊断中的应用研究.计算机工程与应用,2002.9：19-21.
[168]Aylstock F. Neural network false alarm filter. ADA293097. RADC,1995.
[169]王旭,王宏,王文辉.人工神经元网络原理与应用.沈阳：东北大学出版社,2000.12：19-65.
[170]王伟.人工神经网络原理.北京：北京航空航天大学出版社,1995.10：15-132.
[171]John S T, Nello C.模式分析的核方法.赵玲玲等[译].北京：机械工业出版社,2006：89-120.
[172]Theodoridis S, Koutroumbas K.模式识别.李晶皎等,译.北京：电子工业出版社,2006：425-470.
[173]Scholkopf B, Smola A J, Muller K R. Kernel principal component analysis, advances in kernel methods-support vector learning. MIT Press, Cambridge, MA,1999:327-352.
[174]John S T, Nello C. Kernel method for pattern analysis, Cambridge University Press,2004.
[175]David V, Sanchez A. Advanced support vector machines and kernel methods. Neurocomputing,2003:5-20.
[176]褚卫华.模块级电子产品可靠性强化试验方法研究[博士学位论文].长沙：国 防科学技术大学,2003.
[177]王考.基于PQFP的试验剖面优化与缺陷激发效果分析[硕士学位论文].国防科学技术大学,2003.
[178]Yao Q Z, Qu J M, Wu S X. Estimate The thermomechanical life at fatigue of two chip scale packages.1999 IEEE:Electronic Components and Technology Conference,1999.6:797-802.
[179]Neal F E, Thomas J K, Scott A S, Jon R L. Mechanical behaviors of 60/40 tin-lead solder lap joints. IEEE Transactions on components hybrids and manufacturing technology, Vol.12, NO.4,1998.12.
[180]Darveaux R. Effect of simulation methodology on solder joint crack growth correl-ations. Proceedings of 50# Electronic Components & Technology Conference,2000.5:1048-1058.
[181]Wong T E, Reed B A, Cohe H M, Chu D W. Development of BGA solder joint vibration fatigue life prediction model.1999 IEEE:Electronic Components and Technol-ogy Conference,1999.6:149-154.
[182]庄奕琪.微电子器件应用可靠性技术.北京：电子工业出版社,1996.
[183]Doyle B S, Mistry K R. A lifetime prediction method for hot-carrier degradation in surface-channel p-MOS devices. IEEE Trans. Electron Devices 1990.5: 1301-1307.
[184]Hu C, Tam S C, Hsu F C, et al. Hot-electron-induced MOSFET degradation. IEEE Trans. Electron Devices,1985.2:375-384.
[185]Renn S H, et al. Hot carrier effects and reliable lifetime prediction in deep submicron N-and P-Channel SO IMOSFET's. IEEE Trans, Electron Devices, 1998.11:2335-2342.
[186]Son K I, Soma M. Dynamic life-estimation of CMOS ICs in real operating environment:precise electrical method and MLE. IEEE Transactions on reliability,1997.3, vol.46:31-37.
[187]HU C, Tam S C, Hsu F C, et al. Hot-electron induced MOSFET degradation% model, monitor, and improvement. IEEE Trans. Electron Devices,1985.2,Vol.32: 375-385.
[188]Chen I C, Holland S, Hu C. A quantitive physical model for time dependent breakdown in SiO2. IEEE Int Reliab Phys Symp, Orlando, Florida,1985:24-31.
[189]Kaczer B, Degraeve R, Rasras M,et al. Impact of MOSFET gate oxide breakdown on digital circuit operation and reliability. IEEE Trans. Electron Devices,2002:500-506.
[190]MePherson J W, Khamankar R B. Molecular model for intrinsic time-dependent dielectric breakdown in SiO2 dielectrics and the reliability implications for hyper-thin gate oxide. Semicond Sci Technol,2000:462-469.
[191]Montoya J, Levit L, Englisch A. A study of the mechanisms for ESD damage to reticles. Electrical Overstress/Electrostatic Discharge Symposium Proceedings, 2000.9:394-405.
[192]高光渤,李学信.半导体器件可靠性性物理.北京：科学出版社,1987.11：436-490.
[193]季馨.电子设备振动分析与试验.南京：东南大学出版社,1992.10：4-20.
[194]周德俭,潘开林,吴兆华.基于最小能量原理的SMT焊点三维形态预测.电子学报,1999.5：2-3.
[195]朱奇农,王国忠,罗乐.倒装焊中复合SnPb焊点形态模拟.电子学报,2000.5：1-3.
[196]王国忠,王青春,邵琳,钱乙余.片式元件SMT焊点三维形态计算与试验测量.电子工艺技术,1996.5：2-3.
[197]周德俭,潘开林,刘常康.SMT焊点形态成形和焊点可靠性CAD.半导体学报,2002.2：1-4.
[198]张磊,李明雨,王青春.细间距器件焊点桥接研究.电子工艺技术,2001.1：2-3.
[199]黄春跃,周德俭.细微间距器件焊点形态成形建模与预测.电子工艺技术,2000.4：1-4.
[200]Ira R H, James M P, Timothy J S. Effect of solder joint geometry predi-cted fatigue life of BGA solder joints.1996 IEEE Intersociety Conference on Thermal Phenomena,1996:187-184.
[201]Bret A, Zahn. Finite element based solder joint fatigue life predictions for a same die stacked chip scale ball grid array Package.2002 IEEE:International Electronics Manufac-turing Technology Symposium,2002:274-285.
[202]Zhang L L, Chee S S, Maheshwari A, Funcell A. Experimental and finite element analysis of cavity down BGA package solder joint reliability.2000 IEEE:Electr-onics Packaging Technology Conference,2000:391-397.
[203]Andrew M. MOTOLOLA semiconductor technical data:plastic ball grid array(PBGA), MOTOROLA, Inc.1996:1-19.
[204]徐维新.电子设备可靠性热设计指南.北京：电子工业出版社,1995.
[205][美]Suresh S.材料的疲劳(第2版).王光中等[译].北京：国防工业出版社,1999.
[206]张永昌.MSC.Nastran有限元分析理论基础与应用.北京：科学出版社,2004.
[207]温熙森,陈循,唐丙阳.机械系统动态分析理论与应用.长沙：国防科技大学出版社,1997.
[208]金星,洪延姬.工程系统可靠性数值分析方法,北京：国防工业出版社,2002：42-48.
[209]Liao H T, Zhao W B, Guo H R. Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model. Reliability and Maintainability Symposium,2006.1:127-132.
[210]Sathyanarayan G. System level approach for life consumption monitoring of electronics. Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Master of Science,2004.
[211]贺前华,陆以勤,韦岗.一种新的HMM训练方法.电子学报,2000.9：55-57.
[212]曾声奎,赵延弟,张建国.系统可靠性设计分析教程.北京：北京航空航天大学出版社.2000.5：12-135.
[213]何国伟,戴慈庄.可靠性试验技术.北京：国防工业出版社,1995.
[214]胡志强,法庆衍,洪宝林,张越,徐殷.随机振动试验应用技术.北京：中国计量出版社,1996.12.