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
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摘要
故障诊断技术是航天器安全、可靠运行的保障。本文研究了基于有向图模型的航天器故障诊断方法。该方法能够描述复杂系统的故障传播机理以及系统元素间的因果关系,是一种基于深层知识的诊断方法,而且缓解了诊断知识获取瓶颈问题,能够对某些未预知的故障进行识别。因此该方法为航天器故障诊断技术的研究提供了一种新的途径。本论文的主要目的是通过对基于有向图模型的诊断方法的研究,找到适用于航天器在轨自主诊断的方法。为了这样的目的,本论文主要进行如下几方面的研究工作。
针对航天器故障诊断领域的特点,本文将故障传播有向图和符号有向图(SDG)两种系统描述方法的优点结合起来,提出了一个基于分层有向图的定性诊断模型。它具有SDG模型固有的良好完备性,又能够清楚的反映部件间的连接作用关系,同时对该模型采用了有向图分层策略,并利用测试节点间的定性关系来回溯搜索不相容支路找出故障源候选集合,而且可以根据故障可能性对候选故障源进行的排序。最后将该方法应用到卫星一次电源系统,验证了该方法的有效性。
航天器的故障样本和诊断经验的不足,给建模带来了困难,因此,本文采用了根据系统正常的工作模式进行建模的思想,建立系统诊断模型。另外,航天器的许多系统具有多种工作模式,系统关键变量间的定性关系随着工作模式的改变而变化。由于传统SDG模型对这种变化的定性关系的表达还不够完备,采用这种模型进行故障诊断,将会导致诊断分辨率低的问题出现。因此,本文将模糊理论引入到SDG模型中来,提出了一种基于SDG模型和模糊理论相结合的模糊SDG模型。节点变量变为模糊变量,它的每一个模糊子集代表了一种工作模式下的状态;节点间的定性关系通过模糊关系矩阵来表达。通过回溯搜索模糊不相容支路来找出故障源候选集合。实例分析表明了该方法的有效性。
对于前面提出的基于模糊SDG的方法,如果模型中存在不可测量节点,该诊断系统将无法进行诊断推理。为了解决该问题,提出了基于模糊SDG模型和贝叶斯推理相结合的模糊-概率SDG模型。该模型节点间的定性关系用条件概率表(CPT)来表达,通过贝叶斯的不确定性推理和不相容支路的判断,可以在不可测节点存在的情况下,完成不确定性推理,找出故障源候选集合。最后建立了某卫星一次电源系统的诊断模型,故障诊断的仿真结果验证了该方法的有效性。
故障诊断的准确性依赖于系统模型的精确性,为了提高诊断模型的精确性,在以往建模方法的基础上,本文提出了利用定性趋势分析(QTA)技术来辅助建模的方法。趋势分析能够为SDG模型提供充足的定性趋势信息,通过分析这些信息,可以找出节点变量的工作阈值区间,还有节点变量间的多值逻辑关系。最后,本文提出了结合了其它建模方法的综合建模策略,并针对前面提出的模糊-概率SDG模型给出了建模的步骤。将上面提出的方法应用到某卫星一次电源系统中,建立了更为精确的诊断模型,诊断的仿真结果表明该辅助建模方法提高了诊断的准确性。
故障检测的核心问题是传感器的分布问题。针对有向图模型,分别给出了基于可观测性和可靠性的传感器设计方案,并进行了实例分析。最后,通过综合分析前面的研究,提出了新的可靠性设计思想,并对可靠性问题进行了形式化描述,接着给出了基于可观测性和可靠性的传感器分布设计方案。该方案综合考虑了可观测性、可靠性等要求,采用贪婪启发式算法实现了该方案。仿真结果表明该方案满足了可观测性和可靠性的要求,能够快速提高系统的可靠性,更适合系统设计的需要。
Fault diagnosis technique is very important to ensure the safety and reliability of spacecraft. This dissertation studies the spacecraft fault diagnosis based on the digraph model. The approach is a qualitative model-based approach, the model qualitatively describes the characteristic of system structure, and can express cause-effect relation among process variables and propagation relation among the faults, and the model is a deep knowledge-based model, and can solve the bottleneck problem in KA and identify some unknown fault, so the approach provide a new solutions for fault diagnosis of spacecraft. The main purposes of this dissertation are to make a systematic study on digraph-based fault diagnosis technique to realize the on-board autonomous fault diagnosis. For the purpose, the main work is as follows:
According to the characteristic in fault diagnosis domain of spacecraft, this dissertation proposes a novel qualitative diagnosis approach based on hierarchical digraph integrating the goodness of the fault propagation graph and SDG(signed directed graph), the model has the better completeness and explanation facility of SDG, and can express the connection relation among all components. The hierarchical strategy was adopted for digraph so that it reduces search space for failure sources; the fault propagation consistent branches was backward searched by qualitative relations between measured nodes ,and set of failure source candidates were found, the furthermore, the failure source candidates are ranked according to the fault possibility rate. The diagnosis system for primary power system of some satellite was modeled with the approach, and diagnosis simulation result show the approach is valid.
In the aerospace field,because the fault experience and fault data is insufficient, this has brought a lot of difficulties for the fault diagnosis modeling, so the idea that modeling by the data in all normal work modes of system is adapted. In addition, due to the work threshold and relation between processes variables vary with the work state, the tradition SDG model is insufficient expression of the qualitative relation, and has lower resolution of diagnosis. so the fuzzy theory introduced into SDG model, the node variable was expressed as fuzzy variable , each fuzzy subset of which describes the state in each working mode , the cause-effect relationship between the nodes was described by fuzzy relation matrix. The algorithm is presented to identify fuzzy consistent branch by fuzzy reasoning and to search the set of failure source candidates. The validity of approach was identified by the application example.
Under the condition of the unmeasured node, the approach based on the fuzzy SDG model will be not available, In order to solve the problem, the fuzzy probabilistic SDG is proposed based on model of fuzzy SDG and Bayesian inference, the cause-effect relationship between the nodes was described by conditional probabilities table (CPT), the set of failure source candidates is found out by Bayesian inference and backtracking algorithm under the condition of the unmeasured node. The primary electrical power supply system in certain a satellite was modeled with the proposed approach, the diagnosis simulation result show the approach is valid.
The accuracy of diagnosis depends on precision of the system model. To improve the precision of system model, on the basis of the previous method for modeling, the novel modeling method was proposed that the technology of QTA (qualitative trend analysis) can aid in modeling. The qualitative trend information by QTA can transform the valid information for SDG model, the thresholds and dynamic qualitative relation of node variable are determined by analyzing the information, furthermore, the synthetical modeling method combined with other modeling methods is summarized. At last the primary electrical power supply system in certain a satellite was modeled, the diagnosis simulation result show the modeling method improve the precision of the model and the accuracy of the diagnosis .
The core problem of fault detection is the sensor location. According to the digraph model, the design scheme of sensor location based on observability, reliability was respectively given. The applications and analysises based on the schemes were presented. Through the above research, the new design idea of sensor location based on reliability was proposed, and the reliability problem was described again based on the idea, furthermore, the optimal design scheme for location of detection sensors was presented. this scheme takes into account both the fault observability, the reliability of fault detection, and sensor placement costs. The greedy heuristics algorithm was designed for the scheme. Furthermore, the proposed approach was applied to primary electrical power supply system in certain a satellite, and the diagnosis simulation results show that the design scheme has met the design requirements of the observability and the reliability, and can rapidly enhancement reliability and is more suitable for system needs.
针对航天器故障诊断领域的特点,本文将故障传播有向图和符号有向图(SDG)两种系统描述方法的优点结合起来,提出了一个基于分层有向图的定性诊断模型。它具有SDG模型固有的良好完备性,又能够清楚的反映部件间的连接作用关系,同时对该模型采用了有向图分层策略,并利用测试节点间的定性关系来回溯搜索不相容支路找出故障源候选集合,而且可以根据故障可能性对候选故障源进行的排序。最后将该方法应用到卫星一次电源系统,验证了该方法的有效性。
航天器的故障样本和诊断经验的不足,给建模带来了困难,因此,本文采用了根据系统正常的工作模式进行建模的思想,建立系统诊断模型。另外,航天器的许多系统具有多种工作模式,系统关键变量间的定性关系随着工作模式的改变而变化。由于传统SDG模型对这种变化的定性关系的表达还不够完备,采用这种模型进行故障诊断,将会导致诊断分辨率低的问题出现。因此,本文将模糊理论引入到SDG模型中来,提出了一种基于SDG模型和模糊理论相结合的模糊SDG模型。节点变量变为模糊变量,它的每一个模糊子集代表了一种工作模式下的状态;节点间的定性关系通过模糊关系矩阵来表达。通过回溯搜索模糊不相容支路来找出故障源候选集合。实例分析表明了该方法的有效性。
对于前面提出的基于模糊SDG的方法,如果模型中存在不可测量节点,该诊断系统将无法进行诊断推理。为了解决该问题,提出了基于模糊SDG模型和贝叶斯推理相结合的模糊-概率SDG模型。该模型节点间的定性关系用条件概率表(CPT)来表达,通过贝叶斯的不确定性推理和不相容支路的判断,可以在不可测节点存在的情况下,完成不确定性推理,找出故障源候选集合。最后建立了某卫星一次电源系统的诊断模型,故障诊断的仿真结果验证了该方法的有效性。
故障诊断的准确性依赖于系统模型的精确性,为了提高诊断模型的精确性,在以往建模方法的基础上,本文提出了利用定性趋势分析(QTA)技术来辅助建模的方法。趋势分析能够为SDG模型提供充足的定性趋势信息,通过分析这些信息,可以找出节点变量的工作阈值区间,还有节点变量间的多值逻辑关系。最后,本文提出了结合了其它建模方法的综合建模策略,并针对前面提出的模糊-概率SDG模型给出了建模的步骤。将上面提出的方法应用到某卫星一次电源系统中,建立了更为精确的诊断模型,诊断的仿真结果表明该辅助建模方法提高了诊断的准确性。
故障检测的核心问题是传感器的分布问题。针对有向图模型,分别给出了基于可观测性和可靠性的传感器设计方案,并进行了实例分析。最后,通过综合分析前面的研究,提出了新的可靠性设计思想,并对可靠性问题进行了形式化描述,接着给出了基于可观测性和可靠性的传感器分布设计方案。该方案综合考虑了可观测性、可靠性等要求,采用贪婪启发式算法实现了该方案。仿真结果表明该方案满足了可观测性和可靠性的要求,能够快速提高系统的可靠性,更适合系统设计的需要。
Fault diagnosis technique is very important to ensure the safety and reliability of spacecraft. This dissertation studies the spacecraft fault diagnosis based on the digraph model. The approach is a qualitative model-based approach, the model qualitatively describes the characteristic of system structure, and can express cause-effect relation among process variables and propagation relation among the faults, and the model is a deep knowledge-based model, and can solve the bottleneck problem in KA and identify some unknown fault, so the approach provide a new solutions for fault diagnosis of spacecraft. The main purposes of this dissertation are to make a systematic study on digraph-based fault diagnosis technique to realize the on-board autonomous fault diagnosis. For the purpose, the main work is as follows:
According to the characteristic in fault diagnosis domain of spacecraft, this dissertation proposes a novel qualitative diagnosis approach based on hierarchical digraph integrating the goodness of the fault propagation graph and SDG(signed directed graph), the model has the better completeness and explanation facility of SDG, and can express the connection relation among all components. The hierarchical strategy was adopted for digraph so that it reduces search space for failure sources; the fault propagation consistent branches was backward searched by qualitative relations between measured nodes ,and set of failure source candidates were found, the furthermore, the failure source candidates are ranked according to the fault possibility rate. The diagnosis system for primary power system of some satellite was modeled with the approach, and diagnosis simulation result show the approach is valid.
In the aerospace field,because the fault experience and fault data is insufficient, this has brought a lot of difficulties for the fault diagnosis modeling, so the idea that modeling by the data in all normal work modes of system is adapted. In addition, due to the work threshold and relation between processes variables vary with the work state, the tradition SDG model is insufficient expression of the qualitative relation, and has lower resolution of diagnosis. so the fuzzy theory introduced into SDG model, the node variable was expressed as fuzzy variable , each fuzzy subset of which describes the state in each working mode , the cause-effect relationship between the nodes was described by fuzzy relation matrix. The algorithm is presented to identify fuzzy consistent branch by fuzzy reasoning and to search the set of failure source candidates. The validity of approach was identified by the application example.
Under the condition of the unmeasured node, the approach based on the fuzzy SDG model will be not available, In order to solve the problem, the fuzzy probabilistic SDG is proposed based on model of fuzzy SDG and Bayesian inference, the cause-effect relationship between the nodes was described by conditional probabilities table (CPT), the set of failure source candidates is found out by Bayesian inference and backtracking algorithm under the condition of the unmeasured node. The primary electrical power supply system in certain a satellite was modeled with the proposed approach, the diagnosis simulation result show the approach is valid.
The accuracy of diagnosis depends on precision of the system model. To improve the precision of system model, on the basis of the previous method for modeling, the novel modeling method was proposed that the technology of QTA (qualitative trend analysis) can aid in modeling. The qualitative trend information by QTA can transform the valid information for SDG model, the thresholds and dynamic qualitative relation of node variable are determined by analyzing the information, furthermore, the synthetical modeling method combined with other modeling methods is summarized. At last the primary electrical power supply system in certain a satellite was modeled, the diagnosis simulation result show the modeling method improve the precision of the model and the accuracy of the diagnosis .
The core problem of fault detection is the sensor location. According to the digraph model, the design scheme of sensor location based on observability, reliability was respectively given. The applications and analysises based on the schemes were presented. Through the above research, the new design idea of sensor location based on reliability was proposed, and the reliability problem was described again based on the idea, furthermore, the optimal design scheme for location of detection sensors was presented. this scheme takes into account both the fault observability, the reliability of fault detection, and sensor placement costs. The greedy heuristics algorithm was designed for the scheme. Furthermore, the proposed approach was applied to primary electrical power supply system in certain a satellite, and the diagnosis simulation results show that the design scheme has met the design requirements of the observability and the reliability, and can rapidly enhancement reliability and is more suitable for system needs.
引文
1.刘良栋,李果.航天器控制技术进展.控制工程. 2001, (56): 1-9.
2.周前祥,郭华岭.载人航天器故障特点及其诊断技术的研究展望.中国航天, 1999, (9):30-32
3.龙兵,宋立辉,荆武兴,姜兴渭.航天器故障诊断技术回顾与展望.导弹与航天运载技术. 2003, (3):31-38
4. E .B. Douglas, A. D. Gregory, et al. Design of the Remote Agent Experiment for Spacecraft Autonomy. Proceedings of IEEE Aerospace Conference, Snomass, CO , 1998
5.代树武,孙辉先.航天器自主运行技术的进展.宇航学报, 2003, 24(1):17-22.
6.邢琰,吴宏鑫,王晓磊,李智斌.航天器故障诊断与容错控制技术综述.宇航学报. 2003, 24(3):221-226
7. A. K. Chicatelli, W. A. Maul, C. E. Fulton. Propulsion IVHM Technology Experiment. Glenn Research Center, NASA, 2006:1-43
8. D. E. Paris, L.C. Trevino, M. D. Watson, A Framework for Integration of IVHM Technologies for Intelligent Integration for Vehicle Management. Aerospace IEEE Conference. 2005, Montana,:3843– 3852.
9. A. K. Chicatelli, W. A. Maul, C. E. Fulton. Propulsion IVHM Technology Experiment. Glenn Research Center, NASA, 2006:1~43
10.黄文虎,夏松波,刘瑞岩等.设备故障诊断原理、技术及应用.科学出版社, 1996:1-12
11. V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri. A review of process fault detection and diagnosis part I: Quantitative model-based methods. Computers and Chem. 2003, 27: 293—311.
12. V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri. A review of process fault detection and diagnosis part II: Qualitative models and search strategies. Computers and Chem. 2003, 27:313-326.
13. Johan De Kleer and John Seely Brown. A qualitative physics based on confluences. Artificial Intelligence, 1984, 24(1-3):7-84
14. K. D. Forbus. Qualitative Process Theory. Artificial Intelligence, 1984,24:85~168
15. K. D. Forbus, P. Nielsen, B. Faltings. Qualitative Spatial Reasoning: the CLOCK Project. Artificial Intelligence.1991, 51:417~472
16. B. J. Kuipers. Qualitative simulation. Artificial Intelligence. 1986, 29:289~338
17. B. C. Williams, J. DeKleer. Qualitative Reasoning about Physical Systems: a Return to Roots. Artificial Intelligence. 1991, 51:1~7
18.邵晨曦,张俊涛,范金锋,白方周.基于定性定量知识的故障诊断.计算机工程, 2006, 32(06):189-191.
19.白方周,张雷.定性仿真导论.中国科学技术大学出版社,1998:1-59
20.石纯一,廖士中.定性推理方法,清华大学出版社, 2002:1-52
21. M .Kokawa, S. Miyazaki, S. Shingai. Fault location using digraph and inverse direction search with application. Automatica. 1983,19(6):729-735.
22. Tu Fang, K. R. Pattipati, S. Deb, V. N. Malepati. Computationally efficient algorithms for multiple fault diagnosis in large graph - based systems. IEEE Trans. on SMC. 2003, 33(1):73-85.
23. M. S. Azam, F. Tu, K. R. Pattipati, R. Karanam. A Dependency Model-Based Approach for Identifying and Evaluating Power Quality Problems, IEEE Transactions on Power Delivery. 2004, 19(3):1154-1166.
24. S. Deb, i K. R.Pattipat, V. Raghavan, et al. Multi - signal Flow Graphs : A Novel Approach for System Testability Analysis and Fault Diagnosis. IEEE Aerospace and Electronics Magazine. 1995, 10(5):14-25.
25. R. Davis. Diagnostic Reasoning Based on Structure and Behavior. Artificial Intelligence. 1984, 24(1~3):347~410
26. R. Reiter. A theory of diagnosis from first principles. Artificial Intelligence. 1987. 32:57-95
27. J. DeKleer, A. K. Mackworth, R. Reiter. Characterizing diagnosis and systems. Artificial Intelligence. 1992 ,56:197-222.
28. B. C. Williams, P. P. Nayak. A model-based approach to reactive self-configuring systems. In Procs. AAAI. 1996:971–978.
29. B. C. Williams, M. D. Ingham, S. H. Chung, P. H. Elliott. Model-Based Programming of Intelligent Embedded Systems and Robotic Space Explorers. Proceedings of the IEEE. 2003, 91(1):212-237
30. A. Darwiche. Model-based diagnosis using structured system descriptions. Journal of Artificial Intelligence Research. 1998, (8):165-222.
31. E. M. Davidson, S. D. J. McArthur, J. M. McDonald. A Toolset for Applying Model-Based Reasoning Techniques to Diagnostics for Power Systems Protection. IEEE Transactions on Power Systems. 2003, 18(2):680-687
32. N. Sriram, B. Gautam. Model-based diagnosis of hybrid systems. IEEE tansaction on system. 2007, 37(3):348-361
33. B. Peischl, F. Wotawa. Model-based diagnosis or reasoning from first principles. Intelligent Systems, IEEE. 2003, 18(3):32– 37.
34. M. R., Maurya R. Rengaswamy, V. Venkatasubramanian. A signed directed graph-based systematic framework for steady-state malfunction diagnosis inside control loops. Chemical Engineering Science. 2006, 61(6):1790-1810.
35. L.P.Bolduc. X-33 Redundance management system. IEEE aessamit magazine. 2001, 5: 281-285
36. P. Nayak, P. Bernard,et al. Validating the DS1 Remote Agent Experiment. European Space Agency. 1999:349-356
37. Hayden, C. Sandra; Sweet, J. Adam; Christa, E. Scott. Livingstone Model-Based Diagnosis of Earth Observing One. Collection of Technical Papers-AIAA 1st Intelligent Systems Technical Conference. 2004:98-108
38. S. Deb, C. Domagala, et al . Remote diagnosis of the International Space Station utilizing telemetry data. Proceedings of the SPIE Aerosense Conference,Orlando,FL. 2001:60-71
39. Mariela Cerrada, Juan Cardillo, Jose Aguilar and Raúl Faneite. Agents-based design for fault management systems in industrial processes. Computers in Industry. 2007, 58(4):313-328
40. Gang Niu, Tian Han, Bo-Suk Yang and Andy Chit Chiow Tan. Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing. 2007, 21(3):1285-1299.
41. R. Marzi, P. John. Supporting fault diagnosis through a multi-agent-architecture. Mathematics and Computers in Simulation. 2002,60 :217–224
42. T. Yan, J. OTA, T. ARAI, N. KUWAHARA, , Concept Design of Remote Fault Diagnosis System for Autonomous Mobile Robots, Proceedings of the IEEE. 2000:931-936.
43. Wenping Wang, Xiaomin Bai, Wei Zhao, Jian Ding, Zhu Fang. A Multilayer and Distributed Alarm Processing and Fault Diagnosis System Based on Multiagent[C]. IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific. 2005,China, Dalian
44.宋其江,徐敏强,王日新.基于MAS的航天器故障诊断系统模型研究.吉林大学学报工学版.2009.3,39(2):546-550.
45.蒋伟进,许宇胜,吴泉源,孙星明.基于多智能体的分布式智能诊断方法研究.电子学报. 2004, 32(12):235-238.
46. Sterritt Roy, A.Rouff. Christopher, G.Hinchey. Michael, L.Rash. James, Walt Truszkowski. Next generation system and software architectures challenges from future NASA exploration missions. Science of computer programming. 2006, 61:48-57.
47.吴军强,梁军.基于图论的故障诊断技术及其发展.机电工程, 2003, 20(05):188-190.
48.吴重光,夏涛,张贝克.基于符号定向图(SDG)深层知识模型的定性仿真.系统仿真学报. 2003, 15(10):1351-1355.
49.杨帆,萧德云. SDG建模及其应用的进展.控制理论与应用. 2005, 22(5):767-774.
50. J. S. Roger, et al. Objected oriented fault diagnosis for space shuttle main engine redlines. N90-27315
51. W. S. Morris. Advanced fault management for the space station external active thermal control system. A93-41393
52. R. Thurman. Automation of space station thermal control systems-the important role of software. A97-38829
53. D. E. Paris, L.C. Trevino, M. D. Watson. A Framework for Integration of IVHM Technologies for Intelligent Integration for Vehicle Management. Aerospace IEEE Conference. 2005:3843– 385.
54. A. Aljabri, et al. Infusion of autonomy technology into space missions-DS1 lessons learned. A98-34460
55. Sanders G. R, et al. System health management /vehicle health management for future manned space systems. A98-14141.
56. J.T. Malin, J. Kowing, D. Schreckenghost, P. Bonasso, J. Nieten, et al. Multi-Agent Diagnosis and Control of an Air Revitalization System for LifeSupport in Space. Aerospace Conference Proceedings IEEE, 2000, 6:309– 326.
57. Ch. Bonnal, et al. Future reusable launch veh icles in Europe—The FL TP (Future L aunchers Technologies Programme). A 99- 44825.
58. H. J. Hotop, et al. Knowledge-based fault detection and analysis for robots. First results for the ROTEX robots. N90-18472.
59. J., Yoshikawa et al. Research and development of the off gas monitoring device. A01-30585.
60.龙兵,王日新,姜兴渭.基于多信号模型航天器配多故障诊断技术研究.宇航学报,2004,25(5):591-595.
61.范显峰,姜兴渭,黄文虎.基于模型的卫星热控系统故障诊断技术研究.哈尔滨工业大学学报. 2001, 33(3):318~320
62.宝音贺喜格,姜兴渭,黄文虎.基于模型的故障诊断方法在飞船推进系统中的应用.推进技术. 1999, 20(4):5~8
63.荣吉利.基于模型的航天器在轨传感器故障诊断方法.兵工学报,2002,
23(2):242-245.
64.安若铭,姜兴渭,宋政吉.基于功能单元的分层诊断技术研究及在航天器的应用.宇航学报. 2005, 26(4):519-523.
65.陈洪波,宋政吉,姜兴渭.基于组件的模糊推理在卫星故障诊断中的应用.中国空间科学技术. 2004 , 24 ( 1) :56 - 60.
66.王日新,徐敏强,宋政吉.航天器推进系统故障的面向时态检测和诊断.推进技术. 2002, 23(1):11-14.
67.宝音贺喜格,姜兴渭,黄文虎.航天器推进系统故障诊断专家系统的研制开发.推进技术. 1999, 20(4):17-20.
68.宋政吉,姜兴渭,黄文虎.载人航天器推进系统健康监测的组件技术研究.推进技术.2000,21(5):6-8.
69.宋政吉,姜兴渭,王日新.航天器诊断系统的多观测模糊融合技术研究.哈尔滨工业大学学报,2003, 35(7):778-780.
70.朱平,黄文虎,姜兴渭,于百胜,宝音贺喜格.多模型融合故障诊断技术的研究.哈尔滨工业大学学报,2000,32(4):1-3.
71.冯永新,张嘉钟,夏松波,黄文虎.设备故障基于图论的层次诊断模型.哈尔滨工业大学学报,1996,28(3):137-143.
72.代树武,孙辉先.基于模型的飞行器电源故障诊断与故障模式识别.震动与冲击. 2005, 24(3):55-57.
73.刘洪刚,吴建军,陈启智.基于模型的定性推理故障诊断方法研究.系统工程与电子技术. 2002, 6(24): 8-10.
74.刘洪刚,吴建军,陈启智.基于混合知识模型和混合推理策略的液体火箭发动机智能故障诊断.推进技术. 2003, 3(24):194-199
75. S.A.Lapp, G J Powers. Computer-aided Synthesis of Fault-trees. IEEE Transactions on Reliability, 1977, 26(2): 2-12.
76. M. Iri, K. Aoki, E. O’shima , H. Matsuyama. An Algorithm for Diagnosis of System Failures in The Chemical Process. Computer & Chemical Engineering. 1979, 3: 489-493.
77. T. Umeda, T. Kuriyama. A Graphical Approach to Cause and Effect Analysis of Chemical Processing Systems. Chemical Engineering Science, 1980, 35: 2379-2388.
78. J. Shiozaki, H. Matsuyama, K. Tano, E. O’shima. Fault Diagnosis of Chemical Processes by the use of Signed Directed Graphs. Extension to Five-range Patterns of Abnormality. International Chemical Engineering. 1985, 25(4): 651-659.
79. Y Tsuge, J Shiozaki, H Matsuyama, K E O’shima, Y Iguchi, M Fuchigami, M Matsushita. Fesibility Study of a Fault–diagnosis System for Chemical Plants. International Chemical Engineering. 1985, 25(4): 660-667.
80. M A Kramer, B L Palowitch. A Rule-Based Approach to Fault Diagnosis Using the Signed Directed Graph. AIChE Journal. 1987, 33(7): 1607-1078.
81. O. O. OYELEYE , M. A. KRAMER. Qualitative simulation of chemical process systems: Steady-state analysis. AIChE J ournal , 1988 ,34(9) :1441.
82. J. Shiozaki, B. Shibata, H. Matsuyama, E O'shima. Fault diagnosis of chemical processes utilizing signed directed graphs-improvement by using temporal information. Industrial Electronics, IEEE Transactions on. 1989, 36(4):469– 474.
83. C. C. Chang, C.C. Yu. On-Line Fault Diagnosis Using the Signed Directed Graph. Ind. Eng. Chem. Res. 1990, 29(7): 1290-1299.
84. C. C. Yu, C. Lee. Fault Diagnosis Based on Qualitative/Quantitative Process Knowledge. AIChE Journal, 1991, 37(4): 617-628.
85. X. Z. Wang, S. A. Yang, S. H. Yang, C. Mcgreavy. The Application of FuzzyQualitative Simulation in Safety and Operability Assessment of Process Plants. Computers Chemical. Engineering. 1996, 20: 671-676.
86. E. E. Tarifa, N. J. Scenna. Fault Diagnosis, Direct Graphs, and Fuzzy Logic. Computers Chemical. Engineering. 1997, 21: 649-654.
87. N. A. WILCOX, D. M. HIMMELBLAU. The possible cause and effect graphs (PCEG) model for fault diagnosis- I . Methodology . Computers and Chemical Engineering ,1994 ,18(2) :103 - 116.
88. H. VEDAM, V. VENKATASUBRAMANIAN. PCA-SDG based process monitoring and fault diagnosis. Control Engineering Practice. 1999, 7(7) :903 - 917.
89. Y. TSUGE, K. HIRATSUKA, K. TAKEDA, et al. A fault detection and diagnosis for the continuous process with load-fluctuations using orthogonal wavelets . Computers and Chemical Engineering. 2000 ,24(2 - 7) :761 - 767.
90. R. Vaidhyanathan, V. Venkatasubramanian. Experience with an Expert System for Automated HAZOP Analysis. Computers Chemical. Engineering. 1996, 20: 1589-1594.
91. D. Mylaraswamy, V. Venkatasubramanian. A Hybrid Framework for Large Scale Process Fault Diagnosis. Computers Chemical. Engineering. 1997, 21: 935-940.
92. S. Dash, V. Venkatasubramanian. Challenges in the Industrial Applications of Fault Diagnostic Systems. Computers Chemical Engineering. 2000, 24: 785-791.
93. V. Venkatasubramanian, J. Zhao, S. Viswanathan. Intelligent System for HAZOP Analysis of Complex Process Plants. Computers Chemical. Engineering. 2000, 24: 2291-2302.
94. Mano Ram Maurya, Raghunathan Rengaswamy and Venkat Venkatasubramanian. A signed directed graph-based systematic framework for steady-state malfunction diagnosis inside control loops. Chemical Engineering Science. 2006, 61(6): 1790-1810.
95. Mano Ram Maurya, Raghunathan Rengaswamy and Venkat Venkatasubramanian. Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets. Engineering Applications of Artificial Intelligence. 2004, 17(5): 501-518.
96. Hiranmayee Vedam and Venkat Venkatasubramanian. Signed digraph based multiple fault diagnosis. Computers & Chemical Engineering. 1997, 21( S1):655-S660.
97. H. Vedam, V. Venkatasubramanian. Signed Digraph Based Multiple Fault Diagnosis. Computers Chemical. Engineering, 1997, 21: 655-660.
98. KAR S PADAL, OVITS J SZTIPAN, G. KARSAI. Real- time fault diagnostics with multiple aspect models. Proc of the 1991 IEEE Int Conf on Robotics and Automation. 1991,Sacramento :803 - 808.
99. O. O.OYELEYE , F. E. FINCH. Qualitative modeling and fault diagnosis of dynamic processes by MIDAS. Chemical Engineering Communications. 1990 , 96 :205 - 228.
100.刘玥,张贝克,吴重光.基于模糊推理SDG故障诊断新方法.计算机应用. 2005, 25(11):2621-2624.
101.王强,吴重光,张贝克,魏环.基于SDG故障诊断的传感器分布优化设计.计算机仿真. 2006, 23(7):308-312
102.夏涛,张贝克,吴重光.石油化工SDG故障诊断仿真试验系统[J].系统仿真学报. 2003, 15(10): 1377-1380.
103.刘敏华.基于SDG模型的故障诊断及应用研究.清华大学博士论文, 2005.
104.杨帆,萧德云.概率SDG模型及故障分析推理方法.控制与决策. 2006, 21(5):487-492.
105.杨帆,萧德云.大规模复杂系统的定性SDG建模方法.化工自动化及仪表. 2005, 32 (5) : 8-11.
106. Fan Yang; Deyun Xiao. Approach to Fault Diagnosis using SDG Based on Fault Revealing Time. Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on. 2006, (2):5744– 5747.
107.曹文亮.基于符号有向图的热力系统故障诊断方法研究.华北电力大学, 2006.
108. Wen-Liang Cao, Bing-Shu Wang, Liang-Yu Ma, Ji Zhang, Jian-Qiang Gao. Fault diagnosis approach based on the integration of qualitative model and quantitative knowledge of signed directed graph. Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. 2005, (4): 2251 - 2256 .
109. Wen-liang Cao, Bing-shu Wang, Liang-yu Ma, Qin Yan, Wei Hao, Yufeng Xin. Study of fault diagnosis approach based on rules of deep knowledge representation of signed directed graph. Industrial Technology, 2005 ICIT 2005 IEEE International Conference on. 2005:778– 782.
110.刘洪刚,吴建军,陈小前,刘伟强,陈启智.基于SDG的智能故障诊断方法研究.系统工程与电子技术, 2002, 24(1): 103-109.
111. R. Sehgal, O. P. Gandhi, S. Angra. Fault location of tribo-mechanical systems-a graph theory and matrix approach. Reliability Engineering and System Safety. 2000,70:1-14.
112. J. SHIOZAKI , H. MATSUYAMA, E. O’SHIMA, et al . An improved algorithm for iagnosis of system failures in the chemical process. Computers and Chemical Engineering, 1985 , 9 (3) :285 - 293.
113. Zhang Zhaoqian, Wu Chongguang, Zhang Beike, et al. SDG multiple fault diagnosis by real-time inverse inference. Reliability Engineering and System Safety. 2005, 87(2):173-189.
114.沈颂华等.航空航天器供电系统.北京航空航天大学出版社, 2005:22-55.
115. M. R. MAURYA, R. RENGASWAMY, V. VENKATASUBRAMANIAN. Fault diagnosis by qualitative trend analysis of the principal components [J]. Chemical Engineering Research and Design. 2005, 83(A9): 1122–1132.
116. Yong-Guang Ma, Jian-Qiang Gao, Liang-Yu Ma, Qin Yan, Peng Tong. Study on Fault Diagnosis Based on the Qualitative / Quantitative Model of SDG and Genetic Algorithm. Machine Learning and Cybernetics International Conference on. 2006:2053– 2058.
117.诸静.模糊控制理论与系统原理.机械工业出版社,2005.
118. K. W. Przytula, T. D.hompson. Construction of Bayesian networks for diagnostics. Aerospace Conference Proceedings IEEE. 2000, Montana: 193-200.
119. Stuart Russel, peter Norvig.人工智能—一种现代方法.姜哲,等译,人民邮电出版社,2004:
120. F. V. Jesen. Bayesian networks and decision graphs . NewYork : Springer -Verlag , 2001 : 18 - 28.
121. S. Ferat, M. Yavuz, et al. Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization. ParallelComputing, 2007, 33(2):124-143.
122.邹湘文.小卫星电源系统的控制策略及仿真研究.国防科技大学硕士论文. 2006.
123. T. UMEDA , T. KURUYAMA. A graphical approach to cause and effect analysis of chemical processing systems. Chemical Engineering Science. 1980 ,35(12) :2379 - 2388.
124. M. R. MAURYA, R. RENGASWAMY, V. VENKATASUBRAMANIAN. A Systematic Framework for the Development and Analysis of Signed Digraphs for Chemical Processes. 1. Algorithms and Analysis. Industrial and Engineering Chemistry Research. 2003, 42(20): 47892-4810.
125. A. L. STEVEN, G. J. POWERS. Computer-aided Synthesis of Faulttrees. IEEE Transactions on Reliability. 1977, 26(1): 2-12.
126.李安峰,夏涛,张贝克,等.化工过程SDG建模方法.系统仿真学报. 2003, 15 (10) : 1364-1368.
127. S. DASH, R. RENGASWAMY, V. ENKATASUBRAMAN, V. IAN. Fuzzy-logic based trend classification for fault diagnosis of chemical p rocesses. Com puters and Chem ical Engineering. 2003, 27 (3) : 347– 362 .
128. M. R. Mauryaa, R. Rengaswamy, V. Venkatasubramanian. Fault diagnosis using dynamic trend analysis: A review and recent developments. Engineering Applications of Artificial Intelligence 2007 ,(20) :133–146.
129. M. R. MAURYA, R. RENGASWAMY, V. VENKATASUBRAMANIAN. Fault diagnosis by qualitative trend analysis of the principal components. Chemical Engineering Research and Design. 2005.9,83(A9): 1122–1132
130. M.R. Maurya, R. Rengaswamy, V. Venkatasubramanian. A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis. Chemical Engineering Research and Design. 2007, 85(10): 1407-1422.
131. S. DASH, M. R. MAURYA, V. VENKATASUBRAMANIAN. A novel interval-halving framework for automated identification of process trends. AIChE Journal. 2004, 50(1): 149–162.
132.杨帆,萧德云.基于趋势分析和SDG模型的故障诊断.控制理论与应用. 2006, 23(2):306-310.
133. R. RAGHURAJ, M. BHUSHAN, R. ENGASWAMY. Locating sensors incomplex chemical plants based on fault diagnostic observability criteria. American Institute of Chemistry Engineering Journal, 1999, 45(2): 310– 322.
134.冯富宝.集合覆盖问题研究.山东大学硕士论文, 2006.
135. M. BHUSHAN, R. RENGASWAMY. Design of sensor location based on various fault diagnostic observability and reliability criteria. Computers and Chemical Engineering. 2000, 24(2-7): 735– 741.
136.杨帆,萧德云.故障检测的可靠性描述即传感器分布优化算法.应用科学学报. 2006, 24(2):125-130.
137. Sartaj Sahni.数据结构、算法与应用:C++语言描述,汪诗林,孙晓东等译.机械工业出版社, 2000.
2.周前祥,郭华岭.载人航天器故障特点及其诊断技术的研究展望.中国航天, 1999, (9):30-32
3.龙兵,宋立辉,荆武兴,姜兴渭.航天器故障诊断技术回顾与展望.导弹与航天运载技术. 2003, (3):31-38
4. E .B. Douglas, A. D. Gregory, et al. Design of the Remote Agent Experiment for Spacecraft Autonomy. Proceedings of IEEE Aerospace Conference, Snomass, CO , 1998
5.代树武,孙辉先.航天器自主运行技术的进展.宇航学报, 2003, 24(1):17-22.
6.邢琰,吴宏鑫,王晓磊,李智斌.航天器故障诊断与容错控制技术综述.宇航学报. 2003, 24(3):221-226
7. A. K. Chicatelli, W. A. Maul, C. E. Fulton. Propulsion IVHM Technology Experiment. Glenn Research Center, NASA, 2006:1-43
8. D. E. Paris, L.C. Trevino, M. D. Watson, A Framework for Integration of IVHM Technologies for Intelligent Integration for Vehicle Management. Aerospace IEEE Conference. 2005, Montana,:3843– 3852.
9. A. K. Chicatelli, W. A. Maul, C. E. Fulton. Propulsion IVHM Technology Experiment. Glenn Research Center, NASA, 2006:1~43
10.黄文虎,夏松波,刘瑞岩等.设备故障诊断原理、技术及应用.科学出版社, 1996:1-12
11. V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri. A review of process fault detection and diagnosis part I: Quantitative model-based methods. Computers and Chem. 2003, 27: 293—311.
12. V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri. A review of process fault detection and diagnosis part II: Qualitative models and search strategies. Computers and Chem. 2003, 27:313-326.
13. Johan De Kleer and John Seely Brown. A qualitative physics based on confluences. Artificial Intelligence, 1984, 24(1-3):7-84
14. K. D. Forbus. Qualitative Process Theory. Artificial Intelligence, 1984,24:85~168
15. K. D. Forbus, P. Nielsen, B. Faltings. Qualitative Spatial Reasoning: the CLOCK Project. Artificial Intelligence.1991, 51:417~472
16. B. J. Kuipers. Qualitative simulation. Artificial Intelligence. 1986, 29:289~338
17. B. C. Williams, J. DeKleer. Qualitative Reasoning about Physical Systems: a Return to Roots. Artificial Intelligence. 1991, 51:1~7
18.邵晨曦,张俊涛,范金锋,白方周.基于定性定量知识的故障诊断.计算机工程, 2006, 32(06):189-191.
19.白方周,张雷.定性仿真导论.中国科学技术大学出版社,1998:1-59
20.石纯一,廖士中.定性推理方法,清华大学出版社, 2002:1-52
21. M .Kokawa, S. Miyazaki, S. Shingai. Fault location using digraph and inverse direction search with application. Automatica. 1983,19(6):729-735.
22. Tu Fang, K. R. Pattipati, S. Deb, V. N. Malepati. Computationally efficient algorithms for multiple fault diagnosis in large graph - based systems. IEEE Trans. on SMC. 2003, 33(1):73-85.
23. M. S. Azam, F. Tu, K. R. Pattipati, R. Karanam. A Dependency Model-Based Approach for Identifying and Evaluating Power Quality Problems, IEEE Transactions on Power Delivery. 2004, 19(3):1154-1166.
24. S. Deb, i K. R.Pattipat, V. Raghavan, et al. Multi - signal Flow Graphs : A Novel Approach for System Testability Analysis and Fault Diagnosis. IEEE Aerospace and Electronics Magazine. 1995, 10(5):14-25.
25. R. Davis. Diagnostic Reasoning Based on Structure and Behavior. Artificial Intelligence. 1984, 24(1~3):347~410
26. R. Reiter. A theory of diagnosis from first principles. Artificial Intelligence. 1987. 32:57-95
27. J. DeKleer, A. K. Mackworth, R. Reiter. Characterizing diagnosis and systems. Artificial Intelligence. 1992 ,56:197-222.
28. B. C. Williams, P. P. Nayak. A model-based approach to reactive self-configuring systems. In Procs. AAAI. 1996:971–978.
29. B. C. Williams, M. D. Ingham, S. H. Chung, P. H. Elliott. Model-Based Programming of Intelligent Embedded Systems and Robotic Space Explorers. Proceedings of the IEEE. 2003, 91(1):212-237
30. A. Darwiche. Model-based diagnosis using structured system descriptions. Journal of Artificial Intelligence Research. 1998, (8):165-222.
31. E. M. Davidson, S. D. J. McArthur, J. M. McDonald. A Toolset for Applying Model-Based Reasoning Techniques to Diagnostics for Power Systems Protection. IEEE Transactions on Power Systems. 2003, 18(2):680-687
32. N. Sriram, B. Gautam. Model-based diagnosis of hybrid systems. IEEE tansaction on system. 2007, 37(3):348-361
33. B. Peischl, F. Wotawa. Model-based diagnosis or reasoning from first principles. Intelligent Systems, IEEE. 2003, 18(3):32– 37.
34. M. R., Maurya R. Rengaswamy, V. Venkatasubramanian. A signed directed graph-based systematic framework for steady-state malfunction diagnosis inside control loops. Chemical Engineering Science. 2006, 61(6):1790-1810.
35. L.P.Bolduc. X-33 Redundance management system. IEEE aessamit magazine. 2001, 5: 281-285
36. P. Nayak, P. Bernard,et al. Validating the DS1 Remote Agent Experiment. European Space Agency. 1999:349-356
37. Hayden, C. Sandra; Sweet, J. Adam; Christa, E. Scott. Livingstone Model-Based Diagnosis of Earth Observing One. Collection of Technical Papers-AIAA 1st Intelligent Systems Technical Conference. 2004:98-108
38. S. Deb, C. Domagala, et al . Remote diagnosis of the International Space Station utilizing telemetry data. Proceedings of the SPIE Aerosense Conference,Orlando,FL. 2001:60-71
39. Mariela Cerrada, Juan Cardillo, Jose Aguilar and Raúl Faneite. Agents-based design for fault management systems in industrial processes. Computers in Industry. 2007, 58(4):313-328
40. Gang Niu, Tian Han, Bo-Suk Yang and Andy Chit Chiow Tan. Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing. 2007, 21(3):1285-1299.
41. R. Marzi, P. John. Supporting fault diagnosis through a multi-agent-architecture. Mathematics and Computers in Simulation. 2002,60 :217–224
42. T. Yan, J. OTA, T. ARAI, N. KUWAHARA, , Concept Design of Remote Fault Diagnosis System for Autonomous Mobile Robots, Proceedings of the IEEE. 2000:931-936.
43. Wenping Wang, Xiaomin Bai, Wei Zhao, Jian Ding, Zhu Fang. A Multilayer and Distributed Alarm Processing and Fault Diagnosis System Based on Multiagent[C]. IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific. 2005,China, Dalian
44.宋其江,徐敏强,王日新.基于MAS的航天器故障诊断系统模型研究.吉林大学学报工学版.2009.3,39(2):546-550.
45.蒋伟进,许宇胜,吴泉源,孙星明.基于多智能体的分布式智能诊断方法研究.电子学报. 2004, 32(12):235-238.
46. Sterritt Roy, A.Rouff. Christopher, G.Hinchey. Michael, L.Rash. James, Walt Truszkowski. Next generation system and software architectures challenges from future NASA exploration missions. Science of computer programming. 2006, 61:48-57.
47.吴军强,梁军.基于图论的故障诊断技术及其发展.机电工程, 2003, 20(05):188-190.
48.吴重光,夏涛,张贝克.基于符号定向图(SDG)深层知识模型的定性仿真.系统仿真学报. 2003, 15(10):1351-1355.
49.杨帆,萧德云. SDG建模及其应用的进展.控制理论与应用. 2005, 22(5):767-774.
50. J. S. Roger, et al. Objected oriented fault diagnosis for space shuttle main engine redlines. N90-27315
51. W. S. Morris. Advanced fault management for the space station external active thermal control system. A93-41393
52. R. Thurman. Automation of space station thermal control systems-the important role of software. A97-38829
53. D. E. Paris, L.C. Trevino, M. D. Watson. A Framework for Integration of IVHM Technologies for Intelligent Integration for Vehicle Management. Aerospace IEEE Conference. 2005:3843– 385.
54. A. Aljabri, et al. Infusion of autonomy technology into space missions-DS1 lessons learned. A98-34460
55. Sanders G. R, et al. System health management /vehicle health management for future manned space systems. A98-14141.
56. J.T. Malin, J. Kowing, D. Schreckenghost, P. Bonasso, J. Nieten, et al. Multi-Agent Diagnosis and Control of an Air Revitalization System for LifeSupport in Space. Aerospace Conference Proceedings IEEE, 2000, 6:309– 326.
57. Ch. Bonnal, et al. Future reusable launch veh icles in Europe—The FL TP (Future L aunchers Technologies Programme). A 99- 44825.
58. H. J. Hotop, et al. Knowledge-based fault detection and analysis for robots. First results for the ROTEX robots. N90-18472.
59. J., Yoshikawa et al. Research and development of the off gas monitoring device. A01-30585.
60.龙兵,王日新,姜兴渭.基于多信号模型航天器配多故障诊断技术研究.宇航学报,2004,25(5):591-595.
61.范显峰,姜兴渭,黄文虎.基于模型的卫星热控系统故障诊断技术研究.哈尔滨工业大学学报. 2001, 33(3):318~320
62.宝音贺喜格,姜兴渭,黄文虎.基于模型的故障诊断方法在飞船推进系统中的应用.推进技术. 1999, 20(4):5~8
63.荣吉利.基于模型的航天器在轨传感器故障诊断方法.兵工学报,2002,
23(2):242-245.
64.安若铭,姜兴渭,宋政吉.基于功能单元的分层诊断技术研究及在航天器的应用.宇航学报. 2005, 26(4):519-523.
65.陈洪波,宋政吉,姜兴渭.基于组件的模糊推理在卫星故障诊断中的应用.中国空间科学技术. 2004 , 24 ( 1) :56 - 60.
66.王日新,徐敏强,宋政吉.航天器推进系统故障的面向时态检测和诊断.推进技术. 2002, 23(1):11-14.
67.宝音贺喜格,姜兴渭,黄文虎.航天器推进系统故障诊断专家系统的研制开发.推进技术. 1999, 20(4):17-20.
68.宋政吉,姜兴渭,黄文虎.载人航天器推进系统健康监测的组件技术研究.推进技术.2000,21(5):6-8.
69.宋政吉,姜兴渭,王日新.航天器诊断系统的多观测模糊融合技术研究.哈尔滨工业大学学报,2003, 35(7):778-780.
70.朱平,黄文虎,姜兴渭,于百胜,宝音贺喜格.多模型融合故障诊断技术的研究.哈尔滨工业大学学报,2000,32(4):1-3.
71.冯永新,张嘉钟,夏松波,黄文虎.设备故障基于图论的层次诊断模型.哈尔滨工业大学学报,1996,28(3):137-143.
72.代树武,孙辉先.基于模型的飞行器电源故障诊断与故障模式识别.震动与冲击. 2005, 24(3):55-57.
73.刘洪刚,吴建军,陈启智.基于模型的定性推理故障诊断方法研究.系统工程与电子技术. 2002, 6(24): 8-10.
74.刘洪刚,吴建军,陈启智.基于混合知识模型和混合推理策略的液体火箭发动机智能故障诊断.推进技术. 2003, 3(24):194-199
75. S.A.Lapp, G J Powers. Computer-aided Synthesis of Fault-trees. IEEE Transactions on Reliability, 1977, 26(2): 2-12.
76. M. Iri, K. Aoki, E. O’shima , H. Matsuyama. An Algorithm for Diagnosis of System Failures in The Chemical Process. Computer & Chemical Engineering. 1979, 3: 489-493.
77. T. Umeda, T. Kuriyama. A Graphical Approach to Cause and Effect Analysis of Chemical Processing Systems. Chemical Engineering Science, 1980, 35: 2379-2388.
78. J. Shiozaki, H. Matsuyama, K. Tano, E. O’shima. Fault Diagnosis of Chemical Processes by the use of Signed Directed Graphs. Extension to Five-range Patterns of Abnormality. International Chemical Engineering. 1985, 25(4): 651-659.
79. Y Tsuge, J Shiozaki, H Matsuyama, K E O’shima, Y Iguchi, M Fuchigami, M Matsushita. Fesibility Study of a Fault–diagnosis System for Chemical Plants. International Chemical Engineering. 1985, 25(4): 660-667.
80. M A Kramer, B L Palowitch. A Rule-Based Approach to Fault Diagnosis Using the Signed Directed Graph. AIChE Journal. 1987, 33(7): 1607-1078.
81. O. O. OYELEYE , M. A. KRAMER. Qualitative simulation of chemical process systems: Steady-state analysis. AIChE J ournal , 1988 ,34(9) :1441.
82. J. Shiozaki, B. Shibata, H. Matsuyama, E O'shima. Fault diagnosis of chemical processes utilizing signed directed graphs-improvement by using temporal information. Industrial Electronics, IEEE Transactions on. 1989, 36(4):469– 474.
83. C. C. Chang, C.C. Yu. On-Line Fault Diagnosis Using the Signed Directed Graph. Ind. Eng. Chem. Res. 1990, 29(7): 1290-1299.
84. C. C. Yu, C. Lee. Fault Diagnosis Based on Qualitative/Quantitative Process Knowledge. AIChE Journal, 1991, 37(4): 617-628.
85. X. Z. Wang, S. A. Yang, S. H. Yang, C. Mcgreavy. The Application of FuzzyQualitative Simulation in Safety and Operability Assessment of Process Plants. Computers Chemical. Engineering. 1996, 20: 671-676.
86. E. E. Tarifa, N. J. Scenna. Fault Diagnosis, Direct Graphs, and Fuzzy Logic. Computers Chemical. Engineering. 1997, 21: 649-654.
87. N. A. WILCOX, D. M. HIMMELBLAU. The possible cause and effect graphs (PCEG) model for fault diagnosis- I . Methodology . Computers and Chemical Engineering ,1994 ,18(2) :103 - 116.
88. H. VEDAM, V. VENKATASUBRAMANIAN. PCA-SDG based process monitoring and fault diagnosis. Control Engineering Practice. 1999, 7(7) :903 - 917.
89. Y. TSUGE, K. HIRATSUKA, K. TAKEDA, et al. A fault detection and diagnosis for the continuous process with load-fluctuations using orthogonal wavelets . Computers and Chemical Engineering. 2000 ,24(2 - 7) :761 - 767.
90. R. Vaidhyanathan, V. Venkatasubramanian. Experience with an Expert System for Automated HAZOP Analysis. Computers Chemical. Engineering. 1996, 20: 1589-1594.
91. D. Mylaraswamy, V. Venkatasubramanian. A Hybrid Framework for Large Scale Process Fault Diagnosis. Computers Chemical. Engineering. 1997, 21: 935-940.
92. S. Dash, V. Venkatasubramanian. Challenges in the Industrial Applications of Fault Diagnostic Systems. Computers Chemical Engineering. 2000, 24: 785-791.
93. V. Venkatasubramanian, J. Zhao, S. Viswanathan. Intelligent System for HAZOP Analysis of Complex Process Plants. Computers Chemical. Engineering. 2000, 24: 2291-2302.
94. Mano Ram Maurya, Raghunathan Rengaswamy and Venkat Venkatasubramanian. A signed directed graph-based systematic framework for steady-state malfunction diagnosis inside control loops. Chemical Engineering Science. 2006, 61(6): 1790-1810.
95. Mano Ram Maurya, Raghunathan Rengaswamy and Venkat Venkatasubramanian. Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets. Engineering Applications of Artificial Intelligence. 2004, 17(5): 501-518.
96. Hiranmayee Vedam and Venkat Venkatasubramanian. Signed digraph based multiple fault diagnosis. Computers & Chemical Engineering. 1997, 21( S1):655-S660.
97. H. Vedam, V. Venkatasubramanian. Signed Digraph Based Multiple Fault Diagnosis. Computers Chemical. Engineering, 1997, 21: 655-660.
98. KAR S PADAL, OVITS J SZTIPAN, G. KARSAI. Real- time fault diagnostics with multiple aspect models. Proc of the 1991 IEEE Int Conf on Robotics and Automation. 1991,Sacramento :803 - 808.
99. O. O.OYELEYE , F. E. FINCH. Qualitative modeling and fault diagnosis of dynamic processes by MIDAS. Chemical Engineering Communications. 1990 , 96 :205 - 228.
100.刘玥,张贝克,吴重光.基于模糊推理SDG故障诊断新方法.计算机应用. 2005, 25(11):2621-2624.
101.王强,吴重光,张贝克,魏环.基于SDG故障诊断的传感器分布优化设计.计算机仿真. 2006, 23(7):308-312
102.夏涛,张贝克,吴重光.石油化工SDG故障诊断仿真试验系统[J].系统仿真学报. 2003, 15(10): 1377-1380.
103.刘敏华.基于SDG模型的故障诊断及应用研究.清华大学博士论文, 2005.
104.杨帆,萧德云.概率SDG模型及故障分析推理方法.控制与决策. 2006, 21(5):487-492.
105.杨帆,萧德云.大规模复杂系统的定性SDG建模方法.化工自动化及仪表. 2005, 32 (5) : 8-11.
106. Fan Yang; Deyun Xiao. Approach to Fault Diagnosis using SDG Based on Fault Revealing Time. Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on. 2006, (2):5744– 5747.
107.曹文亮.基于符号有向图的热力系统故障诊断方法研究.华北电力大学, 2006.
108. Wen-Liang Cao, Bing-Shu Wang, Liang-Yu Ma, Ji Zhang, Jian-Qiang Gao. Fault diagnosis approach based on the integration of qualitative model and quantitative knowledge of signed directed graph. Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. 2005, (4): 2251 - 2256 .
109. Wen-liang Cao, Bing-shu Wang, Liang-yu Ma, Qin Yan, Wei Hao, Yufeng Xin. Study of fault diagnosis approach based on rules of deep knowledge representation of signed directed graph. Industrial Technology, 2005 ICIT 2005 IEEE International Conference on. 2005:778– 782.
110.刘洪刚,吴建军,陈小前,刘伟强,陈启智.基于SDG的智能故障诊断方法研究.系统工程与电子技术, 2002, 24(1): 103-109.
111. R. Sehgal, O. P. Gandhi, S. Angra. Fault location of tribo-mechanical systems-a graph theory and matrix approach. Reliability Engineering and System Safety. 2000,70:1-14.
112. J. SHIOZAKI , H. MATSUYAMA, E. O’SHIMA, et al . An improved algorithm for iagnosis of system failures in the chemical process. Computers and Chemical Engineering, 1985 , 9 (3) :285 - 293.
113. Zhang Zhaoqian, Wu Chongguang, Zhang Beike, et al. SDG multiple fault diagnosis by real-time inverse inference. Reliability Engineering and System Safety. 2005, 87(2):173-189.
114.沈颂华等.航空航天器供电系统.北京航空航天大学出版社, 2005:22-55.
115. M. R. MAURYA, R. RENGASWAMY, V. VENKATASUBRAMANIAN. Fault diagnosis by qualitative trend analysis of the principal components [J]. Chemical Engineering Research and Design. 2005, 83(A9): 1122–1132.
116. Yong-Guang Ma, Jian-Qiang Gao, Liang-Yu Ma, Qin Yan, Peng Tong. Study on Fault Diagnosis Based on the Qualitative / Quantitative Model of SDG and Genetic Algorithm. Machine Learning and Cybernetics International Conference on. 2006:2053– 2058.
117.诸静.模糊控制理论与系统原理.机械工业出版社,2005.
118. K. W. Przytula, T. D.hompson. Construction of Bayesian networks for diagnostics. Aerospace Conference Proceedings IEEE. 2000, Montana: 193-200.
119. Stuart Russel, peter Norvig.人工智能—一种现代方法.姜哲,等译,人民邮电出版社,2004:
120. F. V. Jesen. Bayesian networks and decision graphs . NewYork : Springer -Verlag , 2001 : 18 - 28.
121. S. Ferat, M. Yavuz, et al. Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization. ParallelComputing, 2007, 33(2):124-143.
122.邹湘文.小卫星电源系统的控制策略及仿真研究.国防科技大学硕士论文. 2006.
123. T. UMEDA , T. KURUYAMA. A graphical approach to cause and effect analysis of chemical processing systems. Chemical Engineering Science. 1980 ,35(12) :2379 - 2388.
124. M. R. MAURYA, R. RENGASWAMY, V. VENKATASUBRAMANIAN. A Systematic Framework for the Development and Analysis of Signed Digraphs for Chemical Processes. 1. Algorithms and Analysis. Industrial and Engineering Chemistry Research. 2003, 42(20): 47892-4810.
125. A. L. STEVEN, G. J. POWERS. Computer-aided Synthesis of Faulttrees. IEEE Transactions on Reliability. 1977, 26(1): 2-12.
126.李安峰,夏涛,张贝克,等.化工过程SDG建模方法.系统仿真学报. 2003, 15 (10) : 1364-1368.
127. S. DASH, R. RENGASWAMY, V. ENKATASUBRAMAN, V. IAN. Fuzzy-logic based trend classification for fault diagnosis of chemical p rocesses. Com puters and Chem ical Engineering. 2003, 27 (3) : 347– 362 .
128. M. R. Mauryaa, R. Rengaswamy, V. Venkatasubramanian. Fault diagnosis using dynamic trend analysis: A review and recent developments. Engineering Applications of Artificial Intelligence 2007 ,(20) :133–146.
129. M. R. MAURYA, R. RENGASWAMY, V. VENKATASUBRAMANIAN. Fault diagnosis by qualitative trend analysis of the principal components. Chemical Engineering Research and Design. 2005.9,83(A9): 1122–1132
130. M.R. Maurya, R. Rengaswamy, V. Venkatasubramanian. A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis. Chemical Engineering Research and Design. 2007, 85(10): 1407-1422.
131. S. DASH, M. R. MAURYA, V. VENKATASUBRAMANIAN. A novel interval-halving framework for automated identification of process trends. AIChE Journal. 2004, 50(1): 149–162.
132.杨帆,萧德云.基于趋势分析和SDG模型的故障诊断.控制理论与应用. 2006, 23(2):306-310.
133. R. RAGHURAJ, M. BHUSHAN, R. ENGASWAMY. Locating sensors incomplex chemical plants based on fault diagnostic observability criteria. American Institute of Chemistry Engineering Journal, 1999, 45(2): 310– 322.
134.冯富宝.集合覆盖问题研究.山东大学硕士论文, 2006.
135. M. BHUSHAN, R. RENGASWAMY. Design of sensor location based on various fault diagnostic observability and reliability criteria. Computers and Chemical Engineering. 2000, 24(2-7): 735– 741.
136.杨帆,萧德云.故障检测的可靠性描述即传感器分布优化算法.应用科学学报. 2006, 24(2):125-130.
137. Sartaj Sahni.数据结构、算法与应用:C++语言描述,汪诗林,孙晓东等译.机械工业出版社, 2000.