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The aim of the paper is to point out the most important factors that should be taken into account during the designing of expert systems for technical diagnostics and advanced condition monitoring. The first such factor is the proper design of unified databases. It was assumed that discussed system consists of a network of coexisting and related nodes containing active statements looking for an equilibrium state. Such network represents a diagnostic model. Diagnostic models describe the relations between observed symptoms and their causes, i.e. the technical states of the object. Direct specification of such models is difficult due to the complex nature of state-symptom relations. An interesting idea is connected with example based inverse diagnostic models. Suggested solutions simplify the development and reduce maintenance costs for the whole system. A very important benefit for industrial application is the opportunity to arrange an incremental development of the final diagnostic expert system.
Słowa kluczowe
Rocznik
Tom
Strony
21--40
Opis fizyczny
Bibliogr. 20, rys., tab., wykr.
Twórcy
autor
- Politechnika Śląska, Wydział Mechaniczny Technologiczny, ul. Konarskiego 18a, 44-100 Gliwice
Bibliografia
- [1] D. Ash. Diagnosis Using Action-Based Hierarchives for Opitmal Real-Time Performance. Ph.D. dessertation, Computer Science Department, Stanford University, 1994.
- [2] N. Carver, V. Lesser. The Evolution of Blackboard Control Architecture CMPSCI Technical Report 92-71 [availabie on the Internet], October 1992.
- [3] W. Cholewa. Frames in diagnostic reanoning Journal of Applied Mathematics and Computer Sciences, 3(3): 595-612, 1993.
- [4] W. Cholewa. Blackboards in diagnostic expert system (in Polish) Pomiary Automatyka Kontrola, 4, 123-128.
- [5] W. Cholewa. Genetic approach to inverse diagnostic modelling Processing: PPAM'99, Kazimierz Dolny, 510-524, 1999.
- [6] W. Cholewa. Example-based inverse diagnostic models. Bulletin of Polish Academy of Science, 49(2): 359-377, 2001.
- [7] W. Cholewa. tle Dała Processing and Reasoning in Technical, WNT Warszawa, 1995.
- [8] W. Cholewa, J. Kiciński, eds. lnverse Diagnostic Models (in Polish) The Silesian University of Technology, Gliwice, 1997.
- [9] W. Cholewa, M.F. White. Inverse modelling in rotordynamics for identification of unbalance distribution. Machine Vibration, 2: 157-167, 1993.
- [10] G.B. Dantzing. Linear Programming and Extensions. Princeton University Press, Princeton 1963.
- [11] W. Cholewa, J. Kiciński, eds. DT200-1 (in Polish). Wydawnictwo IMP PAN. Gdańsk, 1998.
- [12] D. Dubois, H. Prade. Possibility Theory. An Approach to Computerised Processing of Uncertainty. Plenum Press, New York, 1988.
- [13] D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading, 1989.
- [14] A grace. Optimization Toolbox for Use with Matlab. User’s Guide. The MathWorks Inc. Natick, 1995.
- [15] Machinery Information Management Open Systems Alliance MIMOSA – http://www.mimosa.org
- [16] Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin, 1996.
- [17] W. Moczulski. Methods of knwoledge acquisition for the needs of machinery diagnostics (in Polish). Zeszyty Naukowe Politechniki Śląskiej, No. 1382. Wydawnictwo politechniki Śląskiej, Gliwice, 1997.
- [18] M. J. D. Powell. A review of methods for multivariable interpolation at scattered data points. In: I.S. Duff, G.A. Watson, eds., The State of the Art. In Numerical Analysis, 283-309. Clarendon Press, Oxford, 1997.
- [19] W.H. Press, S.A. Teukolski, W.T. Vetterling, B.P. Flannery. Numerical Recipes in C. Cambridge University Press, 1988.
- [20] L.A. Zadeh. The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems. Elsevier Science Publishers. North-Holland 1983.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB2-0006-0051