摘要
最近十多年来,离散事件系统基于模型的诊断受到越来越多研究和工程人员的关注,成为人工智能和控制工程领域的一个热点研究课题。基于模型的诊断主要使用系统的内部结构与行为的知识,根据所得的观测来推理出系统可能的故障信息。
本文主要针对离散事件系统基于模型的诊断方法、系统的可诊断性、不完备模型时的诊断处理方法等,做了如下研究:提出了一种结合时间区间代数对离散事件系统建模、诊断的方法,可以进一步缩小诊断解释空间;充分结合了一种通用的分层概念“D-holon”及诊断分解的概念“无声闭包(Silent closure)”,提出了一种新的概念“SCL-D-holon”,并可用于分层诊断,离线产生所有SCL-D-holons从而可以提高在线诊断的效率;提出了“连续的两个时间窗口”的概念,可以不断地推理出新的观测序列从而进行及时的在线增量诊断;给出了迄今为止几乎所有典型的离散事件系统的可诊断性定义的一个扩展的分层框架,为实际问题中如何选择合适的可诊断性定义及判定方法提供了理论分析及重要参考;于国内外率先对不完备模型下离散事件系统的基于模型诊断理论进行了研究,并扩展提出了自动机的“P-同步积”和“P-诊断”的概念,放松了以往所有文献中总存在的一个明确的或隐含的限定性假设,即待诊断系统的模型是完备的,从而进一步完善了离散事件系统基于模型诊断的理论框架,并给出了一种根据观测来完善系统模型的方法。
There are some severe shortcomings for diagnosis by traditional expert systems. In the middle of 1970s, a new type of intelligent diagnosis method was emerging– model-based diagnosis (MBD). After R. Reiter gave a formalization of MBD with first-order logic, MBD has been widely studied. Earlier, static systems were studied by researchers, and then researches on dynamic systems have begun since the last decade. Especially, model-based diagnosis of discrete-event systems (DESs) has arisen increasing interests, as DESs cover continuous-variable systems which, after quantization, are represented as discrete systems for the purpose of diagnosis at a higher level of abstraction, as well as“discrete by nature”systems.
Several diagnosis methods and diagnosability of DESs are mainly concerned in this paper. In addition, we do some preliminary studies on the way to process the imcomplete DES model.
(1) In the approach to diagnosis of active systems (one kind of DESs) proposed by G. Lamperti et al., only the order of observations of each component is considered, while the order of observations of different components is not concerned. Therefore, the combination of the number of different orders is very large. In addition, there may be some more detailed temporal information in practice, such as the more precise time point or time interval of some observations, the related order of different component events, etc. As a result of the not well-used time information, the search space becomes larger.
Time interval algebra is introduced for representing detailed time information into automata. And active systems can be modeled by this kind of timed automata. At the same time, the corresponding time information is added to observation. As a result of detailed time information, space of the interpretation of the active system is more reduced, and diagnosis efficiency is improved as well.
(2) Computational complexity of multiple fault diagnosis is one of the well-known problems in real-world applications of MBD. And hierarchical diagnosis has been advocated as one of the main remedies for this issue. A hierarchical method of diagnosing DESs is based on the concept“D-holon”presented in by A. Mohammadi Idghamishi and S. Hashtrudi Zad, and each higher hierarchy is a D-holon. But how to produce good D-holons by breaking the system model is not given yet. As we know, there are not many more hierarchical methods to diagnosis for DESs.
The monitor based on silent closures can be seen as a way to decompose a system. As each silent closure in the monitor is built on line, the efficiency would not be very good.
In this paper, we present a novel hierarchical approach to diagnosis of DESs, well combining the concept“D-holon”as the basis of hierarchy, and the concept“silent closure”as each hierarchy. All the new silent closures are built off line, and only the relevant D-holons are used when on line diagnosing the system, thus the efficiency will be improved as well.
(3) Model-based diagnosis of DESs consists in finding what happened to the system from existing observations, under the assumption that the system model is complete. A classical formal way of representing the diagnosis problem is to express it as the synchronized product of the system model automaton and an emitted observation automaton. However, the observations are often uncertain, especially the uncertainty caused by the orders of emission and reception mainly concerned in this paper. The methods of“diagnoser”or its extended variants mainly consist in the compilation of diagnostic information in a diagnoser, which maps observations to failures for on-line diagnosis. These approaches depend on really certain emitted observation sequences, as well as the bridged diagnostic method, otherwise some diagnostic results may be lost; however, it is unrealistic to suppose that observations can be totally ordered according to their emission dates. Moreover, an automata chain of observations for off-line incremental diagnosis, whereas it depends on so-called“sound window”(i.e. each observation emitted in it is required to be received in it, too), just like the decentralized incremental diagnosis, especially in on-line situations. In addition, there is no good approach given to build, on-line and incrementally, the observation automata chain to ensure that the slicing is“correct”whenever the future observations are unknown. How to choose the so-called sound temporal windows is fundamental, or else some diagnostic results may be lost.
In order to obtain the sound windows, an idea proposed by Y. Pencoléis to use domain knowledge, especially knowledge on the properties of the communication channels, in order to split the flow of observations in sound windows. But as said by G. Lamperti and M. Zanella in papers that it is still an unsolved problem by now: how to frame temporal windows in order to achieve completeness of diagnostic results.
In addition, the monotonicity of incremental diagnosis is analyzed from conceptual standpoint in papers by G. Lamperti et al. It is claimed that the criterion of soundness and completeness of results is not meaningful for monitoring-based diagnosis in case the considered observation is affected by temporal uncertainty, since it does not guarantee an important property of monotonicity (Note: the so-called“monotonicity”means monitoring is a shrink and expand process, where one of the previous diagnostic results ?i is first shrunk and then the remaining candidates are possibly extended with additional faults, to eventually make up ?i+1. However, it is thought as non-monotonic when there is not any previous diagnostic result ?i being included by a new diagnostic result ?i+1). And it is proposed that monitoring will be monotonic when an observation is stratified, else it would produce some useless diagnostic results. But, a stratified observation is only a sufficient condition for monotonicity, not necessary. Moreover, there is not a sound approach to make an observation stratified yet. Though the approach of an automata chain of observations proposed by A. Grastien et al. is monotonic, as said before, it is a-posteriori, and there is not any approach proposed for correctly slicing an automaton on-line yet.
In order to overcome the drawbacks of the methods mentioned above, in this paper, an approach to on-line incremental diagnosis of DESs based on two successive temporal windows is proposed. It considers two successive temporal windows at one time to make sure that the observation sequences are sound, complete, and monotonic. It is also timely (the max temporal delay is not more than 2*dmax– here dmax is the maximal transmission delay). In comparison with other related approaches, it is really on-line incremental diagnosis under uncertainty situations universally; especially it is suitable for on-line diagnosis of DESs, when the received observations are too dense to find sound time points (or sound windows) in time.
(4) Generally, diagnosability analysis of DESs is a very important step before online diagnosing DESs. Many researchers have studied this topic from theoretical or practical view, such as global diagnosability, decentralized diagnosability, modular diagnosability, etc. Especially, in recent time, Y. Wang et al. have given a hierarchical framework of a lot of decentralized diagnosability, according to their inner restriction, from theoretical view mainly in the Control Engineering community.
However, in the AI community, there are also emerging many novel definitions of diagnosability for the need of practical diagnosis in literatures. In order to give a deep insight into diagnosability, we have firstly extended the hierarchical framework based on the original one. Then we have studied the evolution of several kinds of finite-state machines (FSMs) from the system model G for diagnosability test, followed by comparing nearly all the approaches to test diagnosability, and so on.
(5) There has been always an assumption in the previous works that the model of the given DES is complete, including all nominal behaviors and all possible failure behaviors. Generally speaking, the assumption is very restrictive, as it is difficult to be assured that the model is complete practically. Also inspired from the way proposed by L. Console et al. to cope with an incomplete system model for static diagnosis, in this paper, we mainly concern model-based diagnosis of a DES with an incomplete system model similarly. A novel concept of“P-synchronization product”for finite state automata is proposed, by which the diagnosis of the DES with an incomplete system model is easily presented. It is also shown that the traditional synchronization product can be seen as a special situation of P-synchronization product. In addition, an approach to completing the system model is discussed as well.
引文
[1] N.Viswanadham, T. L. Johnson. Fault detection and diagnosis of automated manufacturing systems [C]. In: Proc. 27th IEEE Conference on Decision and Control (CDC-88), 1988, 2301–2306.
[2] W. T. Scherer, C. C. White. A survey of expert systems for equipment maintenance and diagnostics [C]. In: M. G. Singh, K. S. Hindi, G. Schmidt, and S. Tzafestas, eds., Fault Detection and Reliability: Knowledge Based & Other Approaches. Pergamon Press, 1987.
[3] W. C. Hamscher, L. Console, and J. de Kleer eds., Readings in Model-based Diagnosis [M]. San Mateo, CA, USA, Morgan-Kaufmann Publishers, 1992.
[4] Diagnostic Competition [OL] [2009-3-6], http://www.dx-competition.org/.
[5] L. Console, O. Dressler. Model-based diagnosis in the real world: lessons learned and challenges remaining [C]. In: Proc. 16th Int’l Joint Conf. on AI (IJCAI-99), Stockholm, Sweden. Morgan-Kaufmann Publishers, 1999, 1393-1400.
[6] P. Struss. Knowledge-based diagnosis: an important challenge and touchstone for AI [C]. In: Proc. 10th European Conference on Artificial Intelligence (ECAI-92), Vienna, 1992, 863-874.
[7] R. Reiter. A theory of diagnosis from first principles [J]. Artificial Intelligence, 1987, 32 (1): 57-96.
[8]李占山,王涛,孙吉贵,林海,冯果忱.利用元件替换测试求诊断[J].软件学报, 2005, 16 (9): 1599-1605.
[9]陈荣,姜云飞.用辩论刻画含约束的诊断空间[J].计算机学报, 2001, 24 (3): 303-307.
[10] J. Crow, J. Rushby. Model-based reconfiguration: diagnosis and recovery [R]. NASA Contractor Report 4596, May 1994.
[11]栾尚敏,戴国忠.利用结构信息的故障诊断方法[J].计算机学报, 2005, 28(5): 801-808.
[12] L. Lin, Y. Jiang. The computation of hitting sets: review and new algorithms. Information Processing Letters, 2003, 86 (4): 177-184.
[13] C. Cassandras, S. Lafortune. Introduction to Discrete Event Systems. Vol. 11 of the Kluwer International Series in Discrete Event Dynamic Systems [M]. Kluwer Academic Publisher, Boston, MA, 1999.
[14] J. Lunze. Discrete-event modelling and diagnosis of quantized dynamical systems [C]. In: Proc. 10th Int’l Workshop on Principles of Diagnosis (DX-99), Loch Awe, United Kingdom, 1999, 147-154.
[15] M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. C. Teneketzis. Diagnosability of discrete event systems [J]. IEEE Transactions on Automatic Control, 1995, 40 (9):1555–1575.
[16] M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. C. Teneketzis. Failure diagnosis using discrete event systems [J]. IEEE Transactions on Control Systems Technology, 1996, 4 (2):105–124.
[17] L. Rozé, M.-O. Cordier. Diagnosing discrete-event systems: extending the“diagnoser approach”to deal with telecommunication networks [J]. Discrete Event Dynamic Systems, 2002, 12 (1): 43–81.
[18] G. Lamperti, M. Zanella. Flexible diagnosis of discrete-event systems by similarity-based reasoning techniques [J]. Artificial Intelligence, 2006, 170 (3): 232-297.
[19] L. Console, C. Picardi, and M. Ribaudo. Process algebras for systems diagnosis [J]. Artificial Intelligence, 2002, 142 (1):19–51.
[20] J. Hillston. A Compositional Approach to Performance Modelling [M]. Cambridge University Press, New York, NY, USA, 1996.
[21] A. Aghasaryan, R. Boubour, E. Fabre, C. Jard, and A. Benveniste. A petri net approach to fault detection and diagnosis in distributed systems [R]. Technical Report 1117, Irisa, 1997.
[22] S. Tripakis. Fault diagnosis for timed automata [C]. In: Proc. 7th International Symposium on Formal Techniques in Real-Time and Fault-Tolerant Systems: Co-sponsored by IFIP WG 2.2, Lecture Notes in Computer Science, 2002, vol. 2469, 205–224.
[23] D. Thorsley, D. Teneketzis. Failure diagnosis of stochastic automata [C]. In: Proc. DX-03, Washington, DC, USA, 2003, 131-136.
[24] D. Thorsley, D. Teneketzis. Diagnosability of stochastic discrete-event systems [J]. IEEE Transactions on Automatic Control, 2005, 50 (4): 476-492.
[25] Y. Pencolé, M. -O. Cordier, and L. Rozé. Decentralized diagnoser approach application to telecommunication networks [C]. In: Proc. DX-00, Morelia, Mexico, 2000, 185-192.
[26] Y. Pencolé, M.-O. Cordier. A formal framework for the decentralised diagnosis of large scale discrete event systems and its application to telecommunication networks [J].Artificial Intelligence, 2005, 164 (1-2): 171-208.
[27] A. Schumann, Y. Pencolé. Efficient on-line failure identification for discrete-event systems [C]. In: Proc. 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process (SAFEPROCESS-06), Beijing, P.R. China, 2006.
[28] R. E. Bryant. Graph-based algorithms for Boolean function manipulation [J]. IEEE Transactions on Computers, 1986, 35 (8): 677–691.
[29] R. E. Bryant. Symbolic Boolean manipulation with Ordered Decision Diagrams [J]. ACM Computing Surveys, 1992, 24 (3): 293-318.
[30] J. Sztipanovits, A. Misra. Diagnosis of discrete event systems using ordered binary decision diagrams [C]. In: Proc. DX-96. Val Morin, Quebec, Canada, 1996.
[31] G. Torta. Compact Representation of Diagnoses for Improving Efficiency in Model Based Diagnosis [D]. Ph.D. dissertation, Dipartimento di Informatica, Universita di Torino, Italy, 2005.
[32] G. Torta, P. Torasso. On the use of OBDDs in model-based diagnosis: An approach based on the partition of the model [J]. Knowledge Based Systems, 2006, 19 (5): 316-323.
[33] P. Torasso, G. Torta. Computing minimum-cardinality diagnoses using OBDDs [C]. Lecture Notes in Artificial Intelligence, 2003, vol. 2821, 224-238.
[34] G. Torta, P. Torasso. The role of OBDDs in controlling the complexity of model based diagnosis [C]. In: Proc. DX-04, Carcassonne, France, 2004, 9-14.
[35] P. Torasso, G. Torta. Model-based diagnosis through OBDD compilation: a complexity analysis[C]. Lecture Notes in Artificial Intelligence, 2006, vol. 4155, 287-305.
[36] A. Schumann, Y. Pencolé, and S. Thiébaux. Diagnosis of discrete-event systems using binary decision diagrams [C]. In: Proc. DX-04, Carcassonne, France, 2004, 197-202.
[37] A. Schumann, Y. Pencolé, and S. Thiébaux. A spectrum of symbolic on-line diagnosis approaches [C]. In: Proc. 22nd American National Conf. on AI (AAAI-07), Vancouver, Canada, 2007, 335-340.
[38] M.-O. Cordier, C. Largou?t. Using model-checking techniques for diagnosing discrete-event systems [C]. In: Proc. DX-01, San Sicario, Italy, 2001, 39-46.
[39] A. Darwiche. Decomposable negation normal form [J]. Journal of the ACM, 2001, 48 (4): 608-647.
[40] A. Darwiche. Model-based diagnosis using structured system descriptions [J]. Journal of Artificial Intelligence Research, 1998, 8: 165-222.
[41] A. Darwiche, G. Provan. Exploiting system structure in model-based diagnosis ofdiscrete-event systems [C]. In: Proc. DX-96, Val Morin, Quebec, Canada, 1996, 95-105.
[42] J. Huang, A. Darwiche. On compiling system models for faster and more scalable diagnosis [C]. In: Proc. AAAI-05, Pittsburgh, Pennsylvania, USA, 2005, 300-306.
[43] A. Darwiche, P. Marquis. A knowledge compilation map [J]. Journal of Artificial Intelligence Research, 2002, vol. 17, 229-264.
[44] A. Malik, P. Struss. Diagnosis of dynamic systems does not necessarily require simulation [C]. In: Proc. DX-96, Val Morin, Quebec, Canada, 1996, 147-156.
[45] P. Struss. Fundamentals of model-based diagnosis of dynamic systems [C]. In: Proc. IJCAI-97, Nagoya, Japan, 1997, 480-485.
[46] D. Dupré, A. Panati. State-based vs. simulation-based diagnosis of dynamic systems [C]. In: Proc. DX-98, Cape Cod, MA, USA, 1998, 40-46.
[47] E. Scarl. Sensor placement for diagnosability [J]. Annals of Mathematics and Artificial Intelligence, 1994, 11 (1-4): 493-510.
[48] L. Travé-Massuyès, T. Escobet, and R. Milne. Model-based diagnosability and sensor placement application to a frame 6 gas turbine subsystem [C]. In: Proc. IJCAI-01, 2001, vol. 1, 551-556.
[49] Y. Yan. Sensor placement and diagnosability analysis at design stage [C]. In: Proc. MONET Workshop on Model-Based Systems at ECAI-04, Valencia, Spain, 2004.
[50] M.-O. Cordier, L. Travé-Massuyès, and X. Pucel. Comparing diagnosability in continuous and discrete-event systems [C]. In: Proc. DX-06, Penaranda de Duero, Burgos, Spain, 2006, 55-60.
[51] S. Jiang, Z. Huang, V. Chandra, and R. Kumar. A polynomial algorithm for testing diagnosability of discrete event systems [J]. IEEE Transactions on Automatic Control, 2001, 46 (8): 1318-1321.
[52] T. -S. Yoo, S. Lafortune. Polynomial-time verification of diagnosability of partially observed discrete-event systems [J]. IEEE Transactions on Automatic Control, 2002, 47 (9): 1491-1495.
[53] Y. Pencolé. Diagnosability analysis of distributed discrete event systems [C]. In: Proc. ECAI-04, Valencia, Spain, 2004, 43-47.
[54] A. Schumann. Towards Efficiently Diagnosing Large Scale Discrete-Event Systems [D]. Ph.D. dissertation, Australian National University, Australia, 2008.
[55] A. Schumann, J. Huang. A scalable jointree algorithm for diagnosability [C]. In: Proc. AAAI-08, AAAI Press, Chicago, USA, 2008, 535-540.
[56] S. Cerutti, G. Lamperti, M. Scaroni, M. Zanella and D. Zanni. A diagnostic environmentfor automaton networks [J]. Software - Practice & Experience, 2007, 37 (4): 365–415.
[57] Y. Pencolé, M.-O. Cordier, and L. Rozé. A decentralized model-based diagnostic tool for complex systems [J]. Internatinal Journal of Artificial Intelligence Tools, 2002, 11 (3): 327–346.
[58] WS-Diamond Project. [OL] [2009-3-6] http://wsdiamond.di.unito.it/.
[59] L. Rozé. Supervision of telecommunication network: a diagnoser approach [C]. In: Proc. DX-97, Mont-Saint-Michel, France, 1997, 103–111.
[60] R. Debouk, S. Lafortune, and D. Teneketzis. A coordinated decentralized protocol for failure diagnosis of discrete event systems [C]. In: Proc. 4th Workshop on Discrete Event Systems (WODES-98), Cagliari, Italy, 1998, 138–143.
[61] P. Baroni, G. Lamperti, P. Pogliano, and M. Zanella. Diagnosis of large active systems [J]. Artificial Intelligence, 1999, 110 (1): 135-183.
[62] R. Alur, D. L. Dill. A theory of timed automata [J]. Theoretical Computer Science, 1994, 126 (2): 183-235.
[63] V. Brusoni, L. Console, P. Terenziani, and D. T. Dupré. A spectrum of definitions for temporal model-based diagnosis [J]. Artificial Intelligence, 1998, 102 (1): 39-79.
[64] J. F. Allen. Maintaining knowledge about temporal intervals [J]. Communications of the ACM, 1983, 26 (11): 832-843.
[65] A. Panati, D. T. Dupré. State-based vs. simulation-based diagnosis of dynamic systems [C]. In: Proc. ECAI-00, Berlin, Germany, 2000, 176-180.
[66] G. Lamperti, M. Zanella. Uncertain temporal observations in diagnosis [C]. In: Proc. ECAI-00, Berlin, Germany, 2000, 151-155.
[67] I. Mozeti?. Hierarchical model-based diagnosis [J]. International Journal of Man-Machine Studies, 1991, 35 (3): 329-362.
[68] L. Chittaro, R. Ranon. Hierarchical model-based diagnosis based on structural abstraction [J]. Artificial Intelligence, 2004, 155 (1-2): 147-182.
[69] A. Feldman, A.J.C. van Gemund and A. Bos. A hybrid approach to hierarchical fault diagnosis [C]. In: Proc. DX-05, Monterey California, USA, 2005, 101-106.
[70] S. Siddiqi, J. Huang. Hierarchical diagnosis of multiple faults [C]. In: Proc. IJCAI-07, India, 2007, 581-586.
[71] R. Su, W. M. Wonham. Hierarchical fault diagnosis for discrete event systems under global consistency [J]. Discrete Event Dynamic Systems, 2006, 16 (1): 39-70.
[72] G. Schullerus, P. Supavatanakul, V. Krebs and J. Lunze. Modelling and hierarchical diagnosis of timed discrete-event systems [J]. Mathematical and Computer Modellingof Dynamical Systems, 2006, 12 (6): 519-542.
[73] A. Mohammadi Idghamishi. Fault Diagnosis in Hierarchical Discrete-Event Systems [D]. MASc thesis, Concordia University, Montreal, QC, Canada, 2004.
[74] A. Mohammadi Idghamishi, S. Hashtrudi Zad. Fault diagnosis in hierarchical discrete-event systems [C]. In: Proc. CDC-04, Atlantis, Paradise Island, Bahamas, 2004, 63-68.
[75] A. Mohammadi Idghamishi, S. Hashtrudi Zad. Hierarchical fault diagnosis: application to an ozone plant [C]. In: Proc. 1st IEEE Conf. on Robotics, Automation and Mechatronics (RAM-04), Singapore, 2004, 734-739.
[76] G. Lamperti, M. Zanella. Continuous diagnosis of discrete-event systems [C]. In: Proc. DX-03, Washington, DC, USA, 2003, 105–111.
[77] G. Lamperti, M. Zanella. A bridged diagnostic method for the monitoring of polymorphic discrete-event systems [J]. IEEE Transactions on Systems Man and Cybernetics - Part B: Cybernetics, 2004, 34 (5): 2222-2244.
[78] Y. Brave, M. Heymann. Control of discrete-event systems modeled as hierarchical state machine [J]. IEEE Transactions on Automatic Control, 1993, 38 (12): 1803-1819.
[79] G. Lamperti, M. Zanella. Diagnosis of Active Systems: Principles and Techniques [M]. Vol. 741 of the Kluwer International Series in Engineering and Computer Science, Kluwer Academic Publishers, Dordrecht, NL, 2003.
[80] G. Lamperti, M. Zanella. Diagnosis of discrete-event systems from uncertain temporal observations [J]. Artificial Intelligence, 2002, 137 (1–2): 91–163.
[81] G. Lamperti, M. Zanella. Monitoring-based diagnosis of discrete-event systems with uncertain observations [C]. In: Proc. QR-05, Graz, Austria, 2005, 97–102.
[82] A. Grastien, M.-O. Cordier, and C. Largou?t. Incremental diagnosis of discrete-event systems [C]. In: Proc. IJCAI-05, Edinburgh, Scotland, 2005, 1564-1665.
[83] M.-O. Cordier, A. Grastien. Exploiting independence in a decentralised and incremental approach of diagnosis [C]. In: Proc. IJCAI-07, India, 2007, 292–297.
[84] G. Lamperti, M. Zanella. On monotonic monitoring of discrete-event systems [C]. In: Proc. DX-07, Nashville, TN, USA, 2007, 130–137.
[85] O. Contant, S. Lafortune, and D. Teneketzis. Diagnosability of discrete event systems with modular structure [J]. Discrete Event Dynamic Systems, 2006, 16 (1): 9-37.
[86] R. Debouk, S. Lafortune, and D. Teneketzis. Coordinated decentralized protocols for failure diagnosis of discrete-event systems [J]. Discrete Event Dynamical Systems, 2000, 10 (1-2): 33-86.
[87] Y. Wang, T.-S. Yoo, and S. Lafortune. Diagnosis of discrete event systems using decentralized architectures [J]. Discrete Event Dynamic Systems, 2007, 17 (2): 233-263.
[88] J. Rintanen, A. Grastien. Diagnosability testing with satisfiability algorithms [C]. In: Proc. IJCAI-07, India, 2007, 532-537.
[89] A. Schumann, and Y. Pencolé. Scalable diagnosability checking of event-driven systems [C]. In: Proc. IJCAI-07, Hyderabad, India, 2007, 575-580.
[90] A. Schumann, W. Mayer and M. Stumptner. Distributed repair of nondiagnosability [C]. In: Proc. ECAI-08, Patras, Greece, 2008, 795-796.
[91] T. Jéron, H. Marchand, S. Pinchinat, and M.-O. Cordier. Supervision patterns in discrete event systems diagnosis [C]. In: Proc. DX-06, Penaranda de Duero, Burgos, Spain, 2006, 117-124.
[92] A. Cimatti, C. Pecheur, and R. Cavada. Formal verification of diagnosability via symbolic model checking [C]. In: Proc. IJCAI-03, Acapulco, Mexico, 2003, 363-369.
[93] R. Sengupta, S. Tripakis. Decentralized diagnosability of regular languages is undecidable [C]. In: Proc. CDC-02, 2002, 423-428.
[94] R. Sengupta. Diagnosis and communication in distributed systems [C]. In: Proc. WODES-98, Cagliari, Italy, 1998, 144-151.
[95] M.-O. Cordier, L. Travé-Massuyès, and X. Pucel. Comparing diagnosability in continuous and discrete-event systems [C]. In: Proc. DX-06, Penaranda de Duero, Burgos, Spain, 2006, 55-60.
[96] W. Qiu, R. Kumar. Decentralized failure diagnosis of discrete event systems [C]. In: Proc. WODES-04, Reims, France, 2004.
[97] Y. Wang, T.-S. Yoo, and S. Lafortune. Decentralized diagnosis of discrete event systems using conditional and unconditional decisions [C]. In: Proc. CDC-05, Seville, Spain, 2005.
[98] M. Sampath, S. Lafortune, and D. Teneketzis. Active diagnosis of discrete-event systems [J]. IEEE Transactions on Automatic Control, 1998, 43 (7): 908-929.
[99] Y. Pencolé, D. Kamenetsky, and A. Schumann. Towards low-cost diagnosis of component-based systems [C]. In: Proc. SAFEPROCESS-06, Beijing, P.R. China, 2006.
[100] E. Kili?. Diagnosability of fuzzy discrete event systems [J]. Information Science, 2008, 178 (3): 858-870.
[101] F. Liu, D. Qiu, H. Xing, and Z. Fan. Diagnosability of fuzzy discrete event systems. CoRR, 2006, abs/cs/0605108. [J/OL] [2009-3-6] http://arxiv.org/abs/cs/0605108.
[102] F. Liu, D. Qiu. Safe diagnosability of stochastic discrete event systems [J]. IEEE Transactions on Automatic Control, 2008, 53 (5): 1291-1296.
[103] C. Barral, S. McIlraith, and T. Son. Formulating diagnostic problem solving using an action language with narratives and sensing [C]. In: Proc. 7th International Conference on Knowledge Representation and Reasoning (KR-00), Breckenridge, CO, 2000, 311-322.
[104] L. Console, C. Picardi and M. Ribaudo. Diagnosis and diagnosability analysis using PEPA [C]. In: Proc. ECAI-00, Berlin, Germany, 2000, 131-135.
[105] M.-O. Cordier, S. Thiébaux. Event-based diagnosis for evolutive systems [C]. In: Proc. DX-94, New Paltz, USA, 1994, 64-69.
[106] L. Console, D. T. Dupré, and P. Torasso. A theory of diagnosis for incomplete causal models [C]. In: Proc. IJCAI-89, Detroit, USA, 1989, 1311-1317.
[107] D. Niebur. Expert Systems for Power System Control in Western Europe [C]. In: Proc. 5th IEEE Symposium on Intelligent Control, Philadelphia, USA, 1990, 1112-1119.
[108] M.-O. Cordier, C. Dousson. Alarm driven monitoring based on chronicles [C]. In: Proc. SAFEPROCESS-00, Budapest, Hungary, 2000, 286-291.
[109] C. Dousson, P. L. Maigat. Chronicle recognition improvement using temporal focusing and hierarchization [C]. In: Proc. IJCAI-07, Hyderabad, India, 2007, 324-329.
[110] J. Gamper, W. Nejdl. Abstract temporal diagnosis in medical domains [J]. Artificial Intelligence in Medicine, 1997, 10 (3): 209-234.
[111] L. Console, C. Picardi, and D. T. Dupré. Temporal decision trees: model-based diagnosis of dynamic systems on-board [J]. Journal of Artificial Intelligence Research, 2003, vol. 19, 469-512.
[112] R. E. Bryant. A methodology for hardware verification based on logic simulation [J]. Journal of the ACM, 1991, 38 (2): 299-328.
[113] R. E. Bryant, M. N. Velev. Boolean satisfiability with transitivity constraints [J]. ACM Transactions on Computational Logic, 2002, 3 (4): 604-627.
[114] P. Kan John, A. Grastien. Local consistency and junction tree for diagnosis of discrete-event systems [C]. In: Proc. ECAI-08, Patras, Greece, 2008, 209-213.
[115] A. Grastien, M.-O. Cordier, and C. Largou?t. First steps towards incremental diagnosis of discrete-event systems [C]. In: Proc. 18th Canadian Conference on Artificial Intelligence (AI-05), Lecture Notes in Artificial Intelligence, 2005, vol. 3501, 170-181.
[116] Y. Pencolé, M.-O. Cordier, and L. Rozé. Incremental decentralized diagnosis approach for the supervision of a telecommunication network [C]. In: Proc. DX-01, San Sicario,Italy, 2001, 151-158.
[117] A. Grastien, Anbulagan. Incremental diagnosis of DES by satisfiability [C]. In: Proc. ECAI-08, Patras, Greece, 2008, 787-788.
[118] P. J. Mosterman. Hybrid Dynamic Systems: a Hybrid Bond Graph Modeling Paradigm and Its Application in Diagnosis [D]. Ph.D. dissertation, Vanderbilt University, 1997.
[119] L. Portinale, D. Magro, and P. Torasso. Multimodal diagnosis combining case-based and model-based reasoning: a formal and experimental analysis [J]. Artificial Intelligence, 2004, 158 (2): 109-153.