基于证据理论的在轨航天器故障诊断方法研究
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摘要
在轨航天器的故障诊断是航天任务中的一项重要技术。随着我国航天事业的蓬勃发展,在轨航天器故障诊断方面不断有新的、更高的需求被提出。对在轨航天器的故障诊断是一项意义重大且具有挑战的研究课题。证据理论作为一种不确定性推理方法,在不确定性信息的表达和推理方面比较灵活便捷,对不确定性信息的处理过程接近人类专家的思维习惯,常被作为信息融合的理论基础,用于根据多源信息进行故障诊断。在轨航天器故障多源信息表示、不确定性、突发性、需要适时处理等特点使得证据理论成为融合诊断的一种合适选择。
本文结合在轨航天器故障诊断需求和证据理论在信息融合方面的优势,对利用证据理论对在轨航天器故障进行诊断的问题进行了研究。针对多源传感器数据在测量在轨航天器故障信息时所呈现出的共同特点,以及融合诊断方面的共性需求,本文设计了利用故障特征生成证据的通用方法和证据理论诊断框架下通用的故障信息提取模式,对在轨航天器的两类典型故障进行了通用方法的针对性实现,进而进行了融合诊断实验。最后,本文还对利用证据理论融合诊断未备案故障的问题进行了探索研究。
利用证据理论进行信息融合时,待融合证据的有效生成和对不确定信息的准确表达是融合的必需条件,组合规则的选择是融合思想的体现。本文第三章设计了基于证据理论的故障融合诊断通用框架,其中包括了利用多传感器数据信息自动生成证据的方法,诊断决策的制定和组合规则的比较与选取。首先,在肯定已有的利用传感器数据生成证据方法的基础上,对其中的缺陷进行了分析,并根据相应思路设计了一种更为合理的证据生成通用方法。其次,提出了融合诊断的决策原则。最后,以信息熵作为不确定性测量标准,对证据理论中的三种常用组合规则进行了评价,得出适用性结论,为本文采用Dempster组合规则进行的融合诊断实验提供了理论依据。
基于证据理论的故障诊断属于决策层信息融合,融合前的决策制定对融合结果有直接影响,对于本文根据多源传感器数据信息直接生成证据的处理方法,多源传感器数据中故障信息的提取便成为了关键问题。本文第四章设计了故障信息的通用提取模式。首先,对在轨航天器故障的特点进行了分析。其次,提出了用于提取数据阶段性信息的诊断窗口概念,并设计了利用阶段性数据的统计量表示故障信息的通用方法。最终,确定了本文用于在轨航天器故障融合诊断的故障信息提取模式。
通用方法的有效性通过对两类有代表性的在轨航天器故障进行融合诊断实验进行验证。本文第五章选用了协同工作类故障和振动发散类故障两类有代表性故障进行实验研究。其中,协同工作类故障的实验数据采用航天实测数据,振动发散类故障的数据选用具有代表性的隔振系统振动发散类故障数据。分别从故障表现、证据生成、融合诊断等方面对诊断过程加以实现,并根据振动发散类故障诊断过程中出现的传感器数据传输出错的问题进行了容错性改进,设计了证据重构方法。
未备案故障的诊断具有重要意义,但利用现有的证据理论融合诊断框架无法实现对未备案故障的诊断。本文第六章对利用证据理论诊断未备案故障的问题进行了探索性研究。首先分析了未备案故障的证据表现形式,进而选取开放世界思想和证据折扣方法形成了融合诊断机理。分别考虑故障焦元无交集和有传递性交集的两种情况,对开放框架下的折扣融合过程进行理论推导,并设计仿真实验验证了方法在诊断未备案故障方面的有效性与合理性。
Fault diagnosis of on-orbit spacecraft is an important technology in space missions. With the flourishing development of China’s space industry, newer and higher requirements in the process of on-orbit spacecraft fault diagnosis are needed. Fault diagnosis of on-orbit spacecraft is a research topic with great meanings and challenges. Evidence theory, as a method of uncertainty reasoning, has flexible and convenient characteristic in expression and reasoning of uncertain information. The treatment process to uncertain information is similar to the thinking habit of human experts. Therefore, evidence theory is usually used in information fusion as the theory basis and can be used for fault diagnosis according to multisource information. Faults in on-orbit spacecrafts have common features, such as multisource information representation, uncertainty, sudden and timely treatment. These features make evidence theory suitable for fusion diagnosis.
Combining the requirements of on-orbit spacecraft fault diagnosis and the advantages of evidence theory in information fusion, fault diagnosis of on-orbit spacecraft based on evidence theory is studied. Common features of multisensor data measured in the on-orbit spacecraft faults and common requirements of fusion diagnosis are considered. General generation of evidence from fault feature is designed and general model of fault information extraction in the diagnosis frame based on evidence theory is proposed. The general methods are specially realized for two kinds of typical faults in on-orbit spacecraft. Fusion diagnosis experiments were carried. Exploratory research of fusion diagnosis of unrecorded fault based on evidence theory is introduced finally.
Effective generation of fusion evidences and accurate expression of uncertain information are essential during information fusion using evidence theory. Choosing of combination rule reflects fusion thought. In Chapter 3, general fusion diagnosis frame based on evidence theory is designed, including automatic generation of evidence according to multisensor data information, decision for diagnosis, and comparison of combination rules. Firstly, an existing method of generation evidence from sensor data is introduced and its disadvantages are analyzed. According to the thinking of the existing method, a more reasonable and general method for evidence generation is designed. Decision principles of fusion diagnosis are proposed then. Three common combination rules of evidence theory are compared and evaluated, while information entropy is chosen as the measurement of uncertain. Applicable conclusions are got and these can be the theoretical basis of using Dempster’s rule in the following fusion diagnosis experiments.
Fault diagnosis based on evidence theory belongs to decision level information fusion. The decisions before fusion directly affect the fusion result. The evidence generation in this thesis directly uses multisensor data information. Extraction of fault information from multisensor data is the key to the question. General fault information extraction model is designed in Chapter 4. Characteristics of on-orbit spacecraft faults are studied. Concept of diagnosis window is proposed then. General expression of fault information, using statistic of data segment, is devised. The fault information extraction model for fusion diagnosis of on-orbit spacecraft fault is designed.
Availability of general method is tested by two kinds of typical faults in on-orbit spacecraft. Cooperative working fault and vibration divergence fault are chosen for the fusion diagnosis experiments in Chapter 5. Cooperative working fault diagnosis experiments use the measured data in the space mission while vibration divergence fault diagnosis experiments use representative vibration isolation system vibration divergence faults data. Diagnosis processes are realized in terms of fault representation, evidence generation and fusion diagnosis. Fault-tolerance improvement is made for the abnormal transmissions of sensor data. Evidence redistribution is designed for the fault-tolerance problem.
Diagnosis of unrecorded fault has important meaning. However, the existing fusion diagnosis frame based on evidence theory can not realize unrecorded faults. Exploratory research of fusion diagnosis of unrecorded fault based on evidence theory is taken in Chapter 6. Evidence expressions of unrecorded fault are analyzed and the“open world”thought and evidence discount method are chosen as the mechanism of fusion diagnosis. Fault focal elements with no intersection and with transitive intersections are considered separately. Theoretical derivations of discount fusion process in open frame are taken. Simulation experiments are designed to verify the effectiveness and rationality of the fusion diagnosis of unrecorded faults.
本文结合在轨航天器故障诊断需求和证据理论在信息融合方面的优势,对利用证据理论对在轨航天器故障进行诊断的问题进行了研究。针对多源传感器数据在测量在轨航天器故障信息时所呈现出的共同特点,以及融合诊断方面的共性需求,本文设计了利用故障特征生成证据的通用方法和证据理论诊断框架下通用的故障信息提取模式,对在轨航天器的两类典型故障进行了通用方法的针对性实现,进而进行了融合诊断实验。最后,本文还对利用证据理论融合诊断未备案故障的问题进行了探索研究。
利用证据理论进行信息融合时,待融合证据的有效生成和对不确定信息的准确表达是融合的必需条件,组合规则的选择是融合思想的体现。本文第三章设计了基于证据理论的故障融合诊断通用框架,其中包括了利用多传感器数据信息自动生成证据的方法,诊断决策的制定和组合规则的比较与选取。首先,在肯定已有的利用传感器数据生成证据方法的基础上,对其中的缺陷进行了分析,并根据相应思路设计了一种更为合理的证据生成通用方法。其次,提出了融合诊断的决策原则。最后,以信息熵作为不确定性测量标准,对证据理论中的三种常用组合规则进行了评价,得出适用性结论,为本文采用Dempster组合规则进行的融合诊断实验提供了理论依据。
基于证据理论的故障诊断属于决策层信息融合,融合前的决策制定对融合结果有直接影响,对于本文根据多源传感器数据信息直接生成证据的处理方法,多源传感器数据中故障信息的提取便成为了关键问题。本文第四章设计了故障信息的通用提取模式。首先,对在轨航天器故障的特点进行了分析。其次,提出了用于提取数据阶段性信息的诊断窗口概念,并设计了利用阶段性数据的统计量表示故障信息的通用方法。最终,确定了本文用于在轨航天器故障融合诊断的故障信息提取模式。
通用方法的有效性通过对两类有代表性的在轨航天器故障进行融合诊断实验进行验证。本文第五章选用了协同工作类故障和振动发散类故障两类有代表性故障进行实验研究。其中,协同工作类故障的实验数据采用航天实测数据,振动发散类故障的数据选用具有代表性的隔振系统振动发散类故障数据。分别从故障表现、证据生成、融合诊断等方面对诊断过程加以实现,并根据振动发散类故障诊断过程中出现的传感器数据传输出错的问题进行了容错性改进,设计了证据重构方法。
未备案故障的诊断具有重要意义,但利用现有的证据理论融合诊断框架无法实现对未备案故障的诊断。本文第六章对利用证据理论诊断未备案故障的问题进行了探索性研究。首先分析了未备案故障的证据表现形式,进而选取开放世界思想和证据折扣方法形成了融合诊断机理。分别考虑故障焦元无交集和有传递性交集的两种情况,对开放框架下的折扣融合过程进行理论推导,并设计仿真实验验证了方法在诊断未备案故障方面的有效性与合理性。
Fault diagnosis of on-orbit spacecraft is an important technology in space missions. With the flourishing development of China’s space industry, newer and higher requirements in the process of on-orbit spacecraft fault diagnosis are needed. Fault diagnosis of on-orbit spacecraft is a research topic with great meanings and challenges. Evidence theory, as a method of uncertainty reasoning, has flexible and convenient characteristic in expression and reasoning of uncertain information. The treatment process to uncertain information is similar to the thinking habit of human experts. Therefore, evidence theory is usually used in information fusion as the theory basis and can be used for fault diagnosis according to multisource information. Faults in on-orbit spacecrafts have common features, such as multisource information representation, uncertainty, sudden and timely treatment. These features make evidence theory suitable for fusion diagnosis.
Combining the requirements of on-orbit spacecraft fault diagnosis and the advantages of evidence theory in information fusion, fault diagnosis of on-orbit spacecraft based on evidence theory is studied. Common features of multisensor data measured in the on-orbit spacecraft faults and common requirements of fusion diagnosis are considered. General generation of evidence from fault feature is designed and general model of fault information extraction in the diagnosis frame based on evidence theory is proposed. The general methods are specially realized for two kinds of typical faults in on-orbit spacecraft. Fusion diagnosis experiments were carried. Exploratory research of fusion diagnosis of unrecorded fault based on evidence theory is introduced finally.
Effective generation of fusion evidences and accurate expression of uncertain information are essential during information fusion using evidence theory. Choosing of combination rule reflects fusion thought. In Chapter 3, general fusion diagnosis frame based on evidence theory is designed, including automatic generation of evidence according to multisensor data information, decision for diagnosis, and comparison of combination rules. Firstly, an existing method of generation evidence from sensor data is introduced and its disadvantages are analyzed. According to the thinking of the existing method, a more reasonable and general method for evidence generation is designed. Decision principles of fusion diagnosis are proposed then. Three common combination rules of evidence theory are compared and evaluated, while information entropy is chosen as the measurement of uncertain. Applicable conclusions are got and these can be the theoretical basis of using Dempster’s rule in the following fusion diagnosis experiments.
Fault diagnosis based on evidence theory belongs to decision level information fusion. The decisions before fusion directly affect the fusion result. The evidence generation in this thesis directly uses multisensor data information. Extraction of fault information from multisensor data is the key to the question. General fault information extraction model is designed in Chapter 4. Characteristics of on-orbit spacecraft faults are studied. Concept of diagnosis window is proposed then. General expression of fault information, using statistic of data segment, is devised. The fault information extraction model for fusion diagnosis of on-orbit spacecraft fault is designed.
Availability of general method is tested by two kinds of typical faults in on-orbit spacecraft. Cooperative working fault and vibration divergence fault are chosen for the fusion diagnosis experiments in Chapter 5. Cooperative working fault diagnosis experiments use the measured data in the space mission while vibration divergence fault diagnosis experiments use representative vibration isolation system vibration divergence faults data. Diagnosis processes are realized in terms of fault representation, evidence generation and fusion diagnosis. Fault-tolerance improvement is made for the abnormal transmissions of sensor data. Evidence redistribution is designed for the fault-tolerance problem.
Diagnosis of unrecorded fault has important meaning. However, the existing fusion diagnosis frame based on evidence theory can not realize unrecorded faults. Exploratory research of fusion diagnosis of unrecorded fault based on evidence theory is taken in Chapter 6. Evidence expressions of unrecorded fault are analyzed and the“open world”thought and evidence discount method are chosen as the mechanism of fusion diagnosis. Fault focal elements with no intersection and with transitive intersections are considered separately. Theoretical derivations of discount fusion process in open frame are taken. Simulation experiments are designed to verify the effectiveness and rationality of the fusion diagnosis of unrecorded faults.
引文
A
Abellan J. 2006. Uncertainty measures on probability intervals from the imprecise Dirichlet model [J]. International Journal of General Systems, 35(5): 509-528. B
Basir O, Yuan X H. 2007. Engine fault diagnosis based on multi-sensor information fusion using Dempster - Shafer evidence theory [J]. Information Fusion, 8(4): 379-386.
Bottone S, Lee D, O’Sullivan M, et al. 2008. Failure prediction and diagnosis for satellite monitoring systems using Bayesian networks [C]. IEEE Conference on Military Communications Conference, San Diego, Canada, Nov. 16-19: 1-7. C
崔俊峰,魏传峰. 2005.基于专家系统的航天器故障诊断地面模拟系统的研制[J].计算机测量与控制, 13(4): 307-309.
崔凯云,韩良斌. 2008.基于仿真系统的航天器故障监视软件设计[J].飞行器测控学报, 27(5): 57-61.
Caster J F, Saleh J H. 2009. Satellite and satellite subsystems reliability: statistical data analysis and modeling [J]. Reliability Engineering and System Safety, 94(11): 1718-1728. D
邓晓彬,谭小野,万成安. 2009.信息融合与多Agent技术在航天器能源管理系统在轨故障诊断中的应用[J].计算机测量与控制, 17(1): 22-24, 27.
邓勇,施文康,朱振福. 2004.一种有效处理冲突证据的组合规则[J].红外与毫米波学报, 23(1): 27-32.
邓勇,朱振福,钟山. 2005.基于证据理论的模糊信息融合及其在目标识别中的应用[J].航空学报, 26(6): 754-758.
Dempster A P. 1967. Upper and lower probabilities induced by a multivalued mapping [J]. Annals of Probabilities, 38(2): 325-339.
Dempster A P. 1968. A generalization of Bayesian inference [J]. Journal of the Royal Statistical Society, Series B, 30(2): 205-232.
Dubois D, Prade H. 1985. A note on measure of specificity for fuzzy sets [J]. International Journal of General Systems, 10(4): 279-283.
Dubois D, Prade H. 1986. A set-theoretic view of belief functions: logical operations and approximations by fuzzy sets [J]. International Journal of General Systems, 12: 193–226.
Dubois D, Prade H. 1988. Representation and combination of uncertainty with belief functions and possibility measures [J]. Computational Intelligence, 4(3): 244-264.
Den?ux T. 1995. A k-nearest neighbor classification rule based on Dempster-Shafer theory [J]. IEEE Transactions on Systems, Man and Cybernetics, 25(5): 804-813.
Den?ux T. 2008. Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence [J]. Artificial Intelligence, 172(2-3): 234-264.
Den?ux T, Smets P. 2006. Classification using belief functions: the relationship between the case-based and model-based approaches [J]. IEEE Transactions on Systems, Man and Cybernetics, Part B, 36(6): 1395–1406. F
Falcoz A, Henry D, Zolghadri A. 2010. Robust fault diagnosis for atmospheric reentry vehicles: a case study [J]. IEEE Transactions on Systems,Man, and Ybernetics, Part A, 40(5): 886-899.
Fan X, Zuo M J. 2006. Fault diagnosis of machines based on D-S evidence theory. Part 1: D-S evidence theory and its improvement [J]. Pattern Recognition Letters, 27(5): 366-76.
Fan X, Zuo M J. 2006. Fault diagnosis of machines based on D-S evidence theory. Part 2: Application of the improved D-S evidence theory in gearbox fault diagnosis [J]. Pattern Recognition Letters, 27(5): 377-85. G
郭华伟,施文康,邓勇,等. 2007.证据冲突:丢弃,发现或化解?[J].系统工程与电子技术, 29(6): 890-898.
Guo H W, Shi W K, Deng Y. 2006. Evaluating sensor reliability in classi?cation problems based on evidence theory [J]. IEEE Transactions on Systems, Man and Cybernetics, Part B, 36(5): 970-981. H
韩崇昭,朱洪艳,段战胜,等. 2006.多源信息融合[M].北京:清华大学出版社.
洪昭艺. 2009.基于DS证据理论的空间目标融合识别方法研究[D]: [硕士].长沙:国防科学技术大学.
胡绍林,陈如山,黄刘生. 2006.航天器故障检测、诊断与容错处理技术研究[J].系统工程与电子技术, 28(9): 1360-1364, 1374.
Han D Q, Dezert J, Han C Z, et al. Is entropy enough to evaluate the probability transformation approach of belief function? [C]. 13th International Conference on Information Fusion, Edinburgh, UK, Jul. 26-29: 1-7.
Harland D M, Lorenz R. 2007.航天系统故障与对策[M].阎列,邓宁丰,舒承东译.北京:中国宇航出版社.
Hartley R V L. 1928. Transmission of information [J]. Bell Systems Technical Journal, 7(3): 535-563. I
Inagaki T. 1991. Interdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory [J]. IEEE Transaction on Reliability, 40(2): 182-188. J
Jousselme A L, Grenier D, Bosséé. 2001. A new distance between two bodies of evidence [J] .Information Fusion, 2(2): 91-101. K
Klir G J. 2006. Uncertainty and information: foundations of generalized information theory [M]. Hoboken, USA: John Wiley and Sons. L
李培华,杨海龙,孙伶俐,等. 2011.灰预测与时间序列模型在航天器故障预测中的应用[J]. 计算机测量与控制, 19(1): 111-113.
刘帆,张玉锋,李明,等. 2010.基于航天器古樟树的过程性诊断方法研究[J].飞行器测控学报, 29(1): 12-16.
Lefevre E, Colot O, Vannoorenberghe P. 2002. Belief function combination and conflict management [J]. Information Fusion, 3(2): 149-162.
Liggins M E, Hall D L, Llinas J (edirors). 2008. Handbook of multisensor data fusion: theory and practice [M]. 2nd ed. Hoboken, USA: Taylor & Francis Group-CRC Press.
Liu W R. Analyzing the degree of conflict among belief functions [J]. Arti?cial Intelligence, 170(11): 909-924.
Lowrance J D, Garvey T D, Strat T M. 1986. A framework for evidential-reasoning systems [C]. 5th National Conference on Arti?cial Intelligence, Philadelphia, USA, Aug. 11-15: 869–901.
M
孟令雷. 2009.磁悬浮隔振器的建模与控制[D]: [硕士].合肥:中国科学技术大学, 39-55.
Martin A, Jousselme A L, Osswald C. 2008. Conflict measure for the discounting operation on belief functions [C]. 11th International Conference on Information Fusion. Cologne, German, Jun. 30-Jul. 3: 1-8.
Mercier D, Den?ux T, Masson M H. 2010. Belief function correction mechanisms [M]// Bouchon-Meunier B, Magdalena L, Ojeda-Aciego M, et al. Foundations of Reasoning under Uncertainty. Berlin: Springer: 203-222.
Mercier D, Quost B, Den?ux T. 2008. Refined modeling of sensor reliability in the belief function framework using contextual discounting [J]. Information Fusion, 9(2): 246-258.
Mo Q Y, Cao J, Gao F. Diagnosis technique based on BP and D-S theory [J]. Advanced Materials Research, 179-180:544-548.
Murphy C K. 2000. Combining belief functions when evidence conflicts [J]. Decision Support Systems, 29(1): 1-9. O
Oukhellou L, Debiolles A, Den?ux T, et al. 2010. Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion [J]. Engineering Applications of Artificial Intelligence, 23(1): 117-128. P
Pirmoradi F, Sassani F, Silva C W. 2007. An efficient algorithm for health monitoring and fault diagnosis in a spacecraft attitude determination system [C]. IEEE International Conference on Systems, Man and Cybernetics. Montreal, Canada, Oct. 7-10: 4024-4030.
Paksoy A, G?ktürk M. 2011. Information fusion with dempster-shafer evidence theory for software defect prediction [J]. Procedia Computer Science, 3:600-605. Q
Quost B, Masson M H, Den?ux T. 2008. Refined classifier combination using belief functions [C]. 11th International Conference on Information Fusion, Cologne, Germany, Jun. 30-Jul. 3: 1-7. R
Raol J R (editor). 2010. Multi-sensor data fusion with Matlab [M]. Hoboken, USA: Taylor & Francis Group-CRC Press.S
宋其江. 2009.基于有向图模型的故障诊断方法研究及其在航天中的应用[D]: [博士].哈尔滨:哈尔滨工业大学.
孙全,叶秀清,顾伟康. 2000一种新的基于证据理论的合成公式[J].电子学报, 28(8): 117-119.
Schubert J. 2011. Conflict management in Dempster–Shafer theory using the degree of falsity [J]. International Journal of Approximate Reasoning, 52(3): 449-460.
Shafer G. 1976. A mathematical theory of evidence [M]. Princeton, USA: Princeton University Press.
Shafer G, Tversky A. 1985. Languages and designs for probability judgment [J]. Cognitive Science, 9(3): 309-339.
Shannon C E. 1948. A mathematical theory of communication [J]. Bell System Tech.nical Journal, 27(4): 379-423, 623–656.
Smarandache F, Dezert J (editors). 2004. Applications and advances of DSmT for information fusion: collected works [M]. Rehoboth, USA: American Research Press.
Smarandache F, Dezert J (editors). 2006. Applications and advances of DSmT for information fusion: collected works [M]. Vol 2. Rehoboth, USA: American Research Press.
Smarandache F, Dezert J (editors). 2009. Applications and advances of DSmT for information fusion: collected works [M]. Vol 3. Rehoboth, USA: American Research Press.
Smets P. 1988. Belief functions versus probability functions [C]. 2nd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems on Uncertainty and Intelligent Systems, Urbino, Italy, Jul. 4-7: 17-24.
Smets P. 2007. Analyzing the combination of conflicting belief functions [J]. Information Fusion, 8(4): 387-412.
Smets P, Kennes R. 1994. .The transferable belief model [J]. Arti?cial Intelligence, 66(2): 191-234. T
Tornqvist D, Saha S, Gustafsson F. 2011. Fault detection using nonlinear parameter estimation [C]. IEEE Aerospace Conference. Big Sky, USA, Mar. 5-12: 1-6.
Tudoroiu N, Khorasani K. 2007. Satellite fault diagnosis using a bank of interacting Kalman filters [J]. IEEE Transactions on Aerospace and Electronic Systems, 43(4): 1334-1350.
Turner R. 2010. Bayesian change point detection for satellite fault prediction [C]. Interdisciplinary Graduate Conference, Cambridge, UK, Jun. 28-29: 1-8.W
魏传峰,李运泽,王浚,等. 2005.航天器热故障诊断专家系统推理机的设计[J].北京航空航天大学学报, 31(1): 60-62.
Waltz E, Linas J. 1990. Multisensor data fusion [M]. Boston, USA: Artech House. Wang H. 2009. Mass function derivation and combination in multivariate data spaces [J]. Information Sciences, 180(6): 813-819.
Wen C L, Wang Y C, Xu X B. 2008. Fuzzy information fusion algorithm of fault diagnosis based on similarity measure of evidence [J]. Advances in Neural Networks, 5264: 506-515. X
解仑,刘帆,巩潇,等. 2009.基于混合智能的航天器故障诊断系统[J].信息与控制, 39(1): 106-113.
邢清华,刘付显. 2009.基于证据折扣优化的冲突证据组合方法[J].系统工程与电子技术, 31(5): 1158-1161.
邢琰,吴宏鑫,王晓磊,等. 2003.航天器故障诊断与容错技术综述[J].宇航学报, 24(3): 221-226.
徐晓滨. 2009.不确定性信息处理的随机集方法及在系统可靠性评估与故障诊断中的应用[D] : [博士].上海:上海海事大学.
Xu Z, Liu M, Yang G, et al. Application of interval analysis and evidence theory to fault location [J]. Electric Power Applications, IET, 3(1): 77-84. Y
杨风暴,王肖霞. 2010. D-S证据理论的冲突证据合成方法[M].北京:国防工业出版社.
杨天社,杨萍,董小社,等. 2008.基于灰色系统理论的航天器故障状态预测方法[J].计算机测量与控制, 16(9): 1284-1285, 1307.
杨建平,黄洪钟,苗强,等. 2008.基于证据理论的航空发动机早期故障诊断方法.航空动力学报, 23(12): 2327-2331.
Yager R R. 1983. Entropy and specificity in a mathematical theory of evidence [J]. International Journal of General Systems, 9(4): 49–260.
Yager R R. 1987. On the Dempster-Shafer framework and new combination rules [J]. Information Sciences, 41(2): 93-137.
Yager R R, Liu L P (editors). 2008. Classic Works of the Dempster-Shafer Theory of Belief Functions [M]. Berlin, German: Springer.
Yang B S, Kim K J. 2006. Application of Dempster-Shafer theory in fault diagnosis of inductionmotors using vibration and current signals [J]. Mechanical Systems and Signal Processing, 20(2): 403-20.
Yang S L, Fu C. 2009. Constructing confidence belief functions from one expert [J]. Expert Systems with Applications, 36(4): 8537-8548.
Yao X H, Fu J Z, Chen Z C. 2009. Intelligent fault diagnosis using rough set method and evidence theory for NC machine tools [J]. International Journal of Computer Integrated Manufacturing, 22(5): 472-482. Z
曾成,赵保军,何佩琨. 2005.不完备识别框架下的证据组合方法[J].电子与信息学报, 27(7): 1043-1046.
肇刚,李言俊. 2010.基于时间序列故障数据挖掘的航天器故障诊断方法[J].飞行器测控学报, 29(3): 1-5.
张平,张小栋. 2010.证据熵在旋转机械故障诊断中的应用[J].振动、测试与诊断, 30(1): 55-58, 98.
张如飞,周军,于晓洲. 2010.基于Maksimov算法的航天器姿控推力器故障诊断[J].振动、测试与诊断, 30(4): 409-413, 473.
朱大奇,于盛林. 2002.基于D-S证据理论的数据融合算法及其在电路故障诊断中的应用[J]. 电子学报, 30(2): 221-223.
朱大奇,杨永清,于盛林. 2004.电子部件故障诊断的Dempster-Shafer信息融合方法[J].控制理论与应用, 21(4): 659-663.
朱大奇,刘永安. 2007.故障诊断的信息融合方法[J].控制与决策, 22(12): 1321-1328. Zadeh L A. 1979. On the validity of Dempster's rule of combination of evidence [M]. Berkely, USA: Electronics Research Laboratory, University of California.
Zhang D Q, Cao J N, Zhou J Y, et al. 2009. Extended Dempster-Shafer theory in context reasoning for ubiquitous computing environments [C]. 12th International Conference on Computational Science and Engineering, Vancouver, Canada, August 29-31: 205-212.
Zhang L W. 1986. Weights of evidence and internal conflict for support functions [J]. Information Science, 38(2): 205-212.
Abellan J. 2006. Uncertainty measures on probability intervals from the imprecise Dirichlet model [J]. International Journal of General Systems, 35(5): 509-528. B
Basir O, Yuan X H. 2007. Engine fault diagnosis based on multi-sensor information fusion using Dempster - Shafer evidence theory [J]. Information Fusion, 8(4): 379-386.
Bottone S, Lee D, O’Sullivan M, et al. 2008. Failure prediction and diagnosis for satellite monitoring systems using Bayesian networks [C]. IEEE Conference on Military Communications Conference, San Diego, Canada, Nov. 16-19: 1-7. C
崔俊峰,魏传峰. 2005.基于专家系统的航天器故障诊断地面模拟系统的研制[J].计算机测量与控制, 13(4): 307-309.
崔凯云,韩良斌. 2008.基于仿真系统的航天器故障监视软件设计[J].飞行器测控学报, 27(5): 57-61.
Caster J F, Saleh J H. 2009. Satellite and satellite subsystems reliability: statistical data analysis and modeling [J]. Reliability Engineering and System Safety, 94(11): 1718-1728. D
邓晓彬,谭小野,万成安. 2009.信息融合与多Agent技术在航天器能源管理系统在轨故障诊断中的应用[J].计算机测量与控制, 17(1): 22-24, 27.
邓勇,施文康,朱振福. 2004.一种有效处理冲突证据的组合规则[J].红外与毫米波学报, 23(1): 27-32.
邓勇,朱振福,钟山. 2005.基于证据理论的模糊信息融合及其在目标识别中的应用[J].航空学报, 26(6): 754-758.
Dempster A P. 1967. Upper and lower probabilities induced by a multivalued mapping [J]. Annals of Probabilities, 38(2): 325-339.
Dempster A P. 1968. A generalization of Bayesian inference [J]. Journal of the Royal Statistical Society, Series B, 30(2): 205-232.
Dubois D, Prade H. 1985. A note on measure of specificity for fuzzy sets [J]. International Journal of General Systems, 10(4): 279-283.
Dubois D, Prade H. 1986. A set-theoretic view of belief functions: logical operations and approximations by fuzzy sets [J]. International Journal of General Systems, 12: 193–226.
Dubois D, Prade H. 1988. Representation and combination of uncertainty with belief functions and possibility measures [J]. Computational Intelligence, 4(3): 244-264.
Den?ux T. 1995. A k-nearest neighbor classification rule based on Dempster-Shafer theory [J]. IEEE Transactions on Systems, Man and Cybernetics, 25(5): 804-813.
Den?ux T. 2008. Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence [J]. Artificial Intelligence, 172(2-3): 234-264.
Den?ux T, Smets P. 2006. Classification using belief functions: the relationship between the case-based and model-based approaches [J]. IEEE Transactions on Systems, Man and Cybernetics, Part B, 36(6): 1395–1406. F
Falcoz A, Henry D, Zolghadri A. 2010. Robust fault diagnosis for atmospheric reentry vehicles: a case study [J]. IEEE Transactions on Systems,Man, and Ybernetics, Part A, 40(5): 886-899.
Fan X, Zuo M J. 2006. Fault diagnosis of machines based on D-S evidence theory. Part 1: D-S evidence theory and its improvement [J]. Pattern Recognition Letters, 27(5): 366-76.
Fan X, Zuo M J. 2006. Fault diagnosis of machines based on D-S evidence theory. Part 2: Application of the improved D-S evidence theory in gearbox fault diagnosis [J]. Pattern Recognition Letters, 27(5): 377-85. G
郭华伟,施文康,邓勇,等. 2007.证据冲突:丢弃,发现或化解?[J].系统工程与电子技术, 29(6): 890-898.
Guo H W, Shi W K, Deng Y. 2006. Evaluating sensor reliability in classi?cation problems based on evidence theory [J]. IEEE Transactions on Systems, Man and Cybernetics, Part B, 36(5): 970-981. H
韩崇昭,朱洪艳,段战胜,等. 2006.多源信息融合[M].北京:清华大学出版社.
洪昭艺. 2009.基于DS证据理论的空间目标融合识别方法研究[D]: [硕士].长沙:国防科学技术大学.
胡绍林,陈如山,黄刘生. 2006.航天器故障检测、诊断与容错处理技术研究[J].系统工程与电子技术, 28(9): 1360-1364, 1374.
Han D Q, Dezert J, Han C Z, et al. Is entropy enough to evaluate the probability transformation approach of belief function? [C]. 13th International Conference on Information Fusion, Edinburgh, UK, Jul. 26-29: 1-7.
Harland D M, Lorenz R. 2007.航天系统故障与对策[M].阎列,邓宁丰,舒承东译.北京:中国宇航出版社.
Hartley R V L. 1928. Transmission of information [J]. Bell Systems Technical Journal, 7(3): 535-563. I
Inagaki T. 1991. Interdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory [J]. IEEE Transaction on Reliability, 40(2): 182-188. J
Jousselme A L, Grenier D, Bosséé. 2001. A new distance between two bodies of evidence [J] .Information Fusion, 2(2): 91-101. K
Klir G J. 2006. Uncertainty and information: foundations of generalized information theory [M]. Hoboken, USA: John Wiley and Sons. L
李培华,杨海龙,孙伶俐,等. 2011.灰预测与时间序列模型在航天器故障预测中的应用[J]. 计算机测量与控制, 19(1): 111-113.
刘帆,张玉锋,李明,等. 2010.基于航天器古樟树的过程性诊断方法研究[J].飞行器测控学报, 29(1): 12-16.
Lefevre E, Colot O, Vannoorenberghe P. 2002. Belief function combination and conflict management [J]. Information Fusion, 3(2): 149-162.
Liggins M E, Hall D L, Llinas J (edirors). 2008. Handbook of multisensor data fusion: theory and practice [M]. 2nd ed. Hoboken, USA: Taylor & Francis Group-CRC Press.
Liu W R. Analyzing the degree of conflict among belief functions [J]. Arti?cial Intelligence, 170(11): 909-924.
Lowrance J D, Garvey T D, Strat T M. 1986. A framework for evidential-reasoning systems [C]. 5th National Conference on Arti?cial Intelligence, Philadelphia, USA, Aug. 11-15: 869–901.
M
孟令雷. 2009.磁悬浮隔振器的建模与控制[D]: [硕士].合肥:中国科学技术大学, 39-55.
Martin A, Jousselme A L, Osswald C. 2008. Conflict measure for the discounting operation on belief functions [C]. 11th International Conference on Information Fusion. Cologne, German, Jun. 30-Jul. 3: 1-8.
Mercier D, Den?ux T, Masson M H. 2010. Belief function correction mechanisms [M]// Bouchon-Meunier B, Magdalena L, Ojeda-Aciego M, et al. Foundations of Reasoning under Uncertainty. Berlin: Springer: 203-222.
Mercier D, Quost B, Den?ux T. 2008. Refined modeling of sensor reliability in the belief function framework using contextual discounting [J]. Information Fusion, 9(2): 246-258.
Mo Q Y, Cao J, Gao F. Diagnosis technique based on BP and D-S theory [J]. Advanced Materials Research, 179-180:544-548.
Murphy C K. 2000. Combining belief functions when evidence conflicts [J]. Decision Support Systems, 29(1): 1-9. O
Oukhellou L, Debiolles A, Den?ux T, et al. 2010. Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion [J]. Engineering Applications of Artificial Intelligence, 23(1): 117-128. P
Pirmoradi F, Sassani F, Silva C W. 2007. An efficient algorithm for health monitoring and fault diagnosis in a spacecraft attitude determination system [C]. IEEE International Conference on Systems, Man and Cybernetics. Montreal, Canada, Oct. 7-10: 4024-4030.
Paksoy A, G?ktürk M. 2011. Information fusion with dempster-shafer evidence theory for software defect prediction [J]. Procedia Computer Science, 3:600-605. Q
Quost B, Masson M H, Den?ux T. 2008. Refined classifier combination using belief functions [C]. 11th International Conference on Information Fusion, Cologne, Germany, Jun. 30-Jul. 3: 1-7. R
Raol J R (editor). 2010. Multi-sensor data fusion with Matlab [M]. Hoboken, USA: Taylor & Francis Group-CRC Press.S
宋其江. 2009.基于有向图模型的故障诊断方法研究及其在航天中的应用[D]: [博士].哈尔滨:哈尔滨工业大学.
孙全,叶秀清,顾伟康. 2000一种新的基于证据理论的合成公式[J].电子学报, 28(8): 117-119.
Schubert J. 2011. Conflict management in Dempster–Shafer theory using the degree of falsity [J]. International Journal of Approximate Reasoning, 52(3): 449-460.
Shafer G. 1976. A mathematical theory of evidence [M]. Princeton, USA: Princeton University Press.
Shafer G, Tversky A. 1985. Languages and designs for probability judgment [J]. Cognitive Science, 9(3): 309-339.
Shannon C E. 1948. A mathematical theory of communication [J]. Bell System Tech.nical Journal, 27(4): 379-423, 623–656.
Smarandache F, Dezert J (editors). 2004. Applications and advances of DSmT for information fusion: collected works [M]. Rehoboth, USA: American Research Press.
Smarandache F, Dezert J (editors). 2006. Applications and advances of DSmT for information fusion: collected works [M]. Vol 2. Rehoboth, USA: American Research Press.
Smarandache F, Dezert J (editors). 2009. Applications and advances of DSmT for information fusion: collected works [M]. Vol 3. Rehoboth, USA: American Research Press.
Smets P. 1988. Belief functions versus probability functions [C]. 2nd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems on Uncertainty and Intelligent Systems, Urbino, Italy, Jul. 4-7: 17-24.
Smets P. 2007. Analyzing the combination of conflicting belief functions [J]. Information Fusion, 8(4): 387-412.
Smets P, Kennes R. 1994. .The transferable belief model [J]. Arti?cial Intelligence, 66(2): 191-234. T
Tornqvist D, Saha S, Gustafsson F. 2011. Fault detection using nonlinear parameter estimation [C]. IEEE Aerospace Conference. Big Sky, USA, Mar. 5-12: 1-6.
Tudoroiu N, Khorasani K. 2007. Satellite fault diagnosis using a bank of interacting Kalman filters [J]. IEEE Transactions on Aerospace and Electronic Systems, 43(4): 1334-1350.
Turner R. 2010. Bayesian change point detection for satellite fault prediction [C]. Interdisciplinary Graduate Conference, Cambridge, UK, Jun. 28-29: 1-8.W
魏传峰,李运泽,王浚,等. 2005.航天器热故障诊断专家系统推理机的设计[J].北京航空航天大学学报, 31(1): 60-62.
Waltz E, Linas J. 1990. Multisensor data fusion [M]. Boston, USA: Artech House. Wang H. 2009. Mass function derivation and combination in multivariate data spaces [J]. Information Sciences, 180(6): 813-819.
Wen C L, Wang Y C, Xu X B. 2008. Fuzzy information fusion algorithm of fault diagnosis based on similarity measure of evidence [J]. Advances in Neural Networks, 5264: 506-515. X
解仑,刘帆,巩潇,等. 2009.基于混合智能的航天器故障诊断系统[J].信息与控制, 39(1): 106-113.
邢清华,刘付显. 2009.基于证据折扣优化的冲突证据组合方法[J].系统工程与电子技术, 31(5): 1158-1161.
邢琰,吴宏鑫,王晓磊,等. 2003.航天器故障诊断与容错技术综述[J].宇航学报, 24(3): 221-226.
徐晓滨. 2009.不确定性信息处理的随机集方法及在系统可靠性评估与故障诊断中的应用[D] : [博士].上海:上海海事大学.
Xu Z, Liu M, Yang G, et al. Application of interval analysis and evidence theory to fault location [J]. Electric Power Applications, IET, 3(1): 77-84. Y
杨风暴,王肖霞. 2010. D-S证据理论的冲突证据合成方法[M].北京:国防工业出版社.
杨天社,杨萍,董小社,等. 2008.基于灰色系统理论的航天器故障状态预测方法[J].计算机测量与控制, 16(9): 1284-1285, 1307.
杨建平,黄洪钟,苗强,等. 2008.基于证据理论的航空发动机早期故障诊断方法.航空动力学报, 23(12): 2327-2331.
Yager R R. 1983. Entropy and specificity in a mathematical theory of evidence [J]. International Journal of General Systems, 9(4): 49–260.
Yager R R. 1987. On the Dempster-Shafer framework and new combination rules [J]. Information Sciences, 41(2): 93-137.
Yager R R, Liu L P (editors). 2008. Classic Works of the Dempster-Shafer Theory of Belief Functions [M]. Berlin, German: Springer.
Yang B S, Kim K J. 2006. Application of Dempster-Shafer theory in fault diagnosis of inductionmotors using vibration and current signals [J]. Mechanical Systems and Signal Processing, 20(2): 403-20.
Yang S L, Fu C. 2009. Constructing confidence belief functions from one expert [J]. Expert Systems with Applications, 36(4): 8537-8548.
Yao X H, Fu J Z, Chen Z C. 2009. Intelligent fault diagnosis using rough set method and evidence theory for NC machine tools [J]. International Journal of Computer Integrated Manufacturing, 22(5): 472-482. Z
曾成,赵保军,何佩琨. 2005.不完备识别框架下的证据组合方法[J].电子与信息学报, 27(7): 1043-1046.
肇刚,李言俊. 2010.基于时间序列故障数据挖掘的航天器故障诊断方法[J].飞行器测控学报, 29(3): 1-5.
张平,张小栋. 2010.证据熵在旋转机械故障诊断中的应用[J].振动、测试与诊断, 30(1): 55-58, 98.
张如飞,周军,于晓洲. 2010.基于Maksimov算法的航天器姿控推力器故障诊断[J].振动、测试与诊断, 30(4): 409-413, 473.
朱大奇,于盛林. 2002.基于D-S证据理论的数据融合算法及其在电路故障诊断中的应用[J]. 电子学报, 30(2): 221-223.
朱大奇,杨永清,于盛林. 2004.电子部件故障诊断的Dempster-Shafer信息融合方法[J].控制理论与应用, 21(4): 659-663.
朱大奇,刘永安. 2007.故障诊断的信息融合方法[J].控制与决策, 22(12): 1321-1328. Zadeh L A. 1979. On the validity of Dempster's rule of combination of evidence [M]. Berkely, USA: Electronics Research Laboratory, University of California.
Zhang D Q, Cao J N, Zhou J Y, et al. 2009. Extended Dempster-Shafer theory in context reasoning for ubiquitous computing environments [C]. 12th International Conference on Computational Science and Engineering, Vancouver, Canada, August 29-31: 205-212.
Zhang L W. 1986. Weights of evidence and internal conflict for support functions [J]. Information Science, 38(2): 205-212.