DEVELOPMENT OF AN INFORMATION SYSTEM FOR DECISION SUPPORT AND AUTOMATION OF CONTROL OF TV3-117 AIRCRAFT ENGINE IN CRITICAL SITUATIONS BASED ON KNOWLEDGE ENGINEERING
DOI:
https://doi.org/10.18372/2306-1472.82.14610Keywords:
aircraft engine, information system, object-oriented models, critical situations, ontologyAbstract
Purpose: The purpose of this article is to develop an information system for decision support and automation of control of the aircraft engine TV3-117 in critical situations based on knowledge engineering. Methods: The following methods are applied in the article: simulation modeling method at the stage of designing an information system for decision support; special methods and means of object-oriented modeling of the subject area, which are developed for the design of information systems in order to recreate the conceptual model of experts in a formalized model of knowledge representation; hierarchical search method to search for use cases; ontological analysis with the aim of identifying and combining relevant information-logical and functional aspects of the system under study. In the modeling process, paradigmatic relationships are established between the cognitive elements of the process of controlling a complex dynamic object in critical situations (cause-effect, similarities), as well as generalization, association, depending on the implementation, necessary for the development of a complex of object-oriented models of the control process. Results: The conducted studies show that an additional analysis of all the possibilities of the applied knowledge representation models is needed to solve specific problems in the considered problem area. The methodology of object-cognitive analysis is the basis for creating an information system for decision support, including the intellectual component of the acquisition, accumulation, processing, provision, updating and dissemination of knowledge. The obtained object-oriented models of the subject area and ontology of the decision support system are the basis for the development of methods and algorithms for finding management solutions in critical situations. Discussion: The results obtained are applied within the framework of the concept of intellectualizing the process of control and diagnostics of the TV3-117 aircraft engine technical state in flight modes, one of the points of which is the intellectual processing and storage of information about the results of flight tests and operation of the TV3-117 aircraft engine based on the requirements of modern databases and knowledge bases, with the possibility of their integration into modern CASE-technologies.
References
Kinneer C., Coker Z., Wang J., Garlan D. and Goues L.C. (2018). Managing uncertainty in self-adaptive systems with plan reuse and stochastic search, Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems, pp. 40-50. https://doi.org/10.1145/3194133.3194145
Haddad W.M. and Corrado J.R. (2000). Robust resilient dynamic controllers for systems with parametric uncertainty and controller gain variations, International journal of control, vol. 73, issue 15, pp. 1405-1423. https://doi.org/10.1080/002071700445424
Badamshin R.A., Ilyasov B.G. and Chernyakhovskaya L.R. (2003). Problemy upravleniya slozhnymi dinamicheskimi obektami v kriticheskikh situacziyakh na osnove znanij [Problems of managing complex dynamic objects in critical situations based on knowledge]. Moscow: Engineering, 240 p. (in Russian)
Michaelides-Mateou S. and Mateou A. (2010). Flying in the Face of Criminalization : The Safety Implications of Prosecuting Aviation Professionals for Accidents, United Kingdom: Taylor & Francis Ltd, 234 p.
Nixon J. and Braithwaite G.R. (2018). What do aircraft accident investigators do and what makes them good at it? Developing a competency framework for investigators using grounded theory, Safety Science, vol. 103, pp. 153-161. https://doi.org/10.1016/j.ssci.2017.11.017
Fedunov B.E. (2016). Tactical-level onboard real-time advisory expert systems for manned aircraft as development and maintenance entities, Journal of computer and systems sciences international, vol. 55, pp. 579-597. https://doi.org/10.1134/S1064230716040055
Petrovas A. and Rinkeviciene R. (2012). Automatic Control Theory I, II: A Laboratory Manual, Vilnius: Technika, 98 p. https://doi.org/10.3846/1335-S
Pontryagin L.S. (2018). Mathematical Theory of Optimal Processes, London: Routledge, 360 p. https://doi.org/10.1201/9780203749319
Cheung A., Ip W.H. and Lu D. (2005). Expert system for aircraft maintenance services industry, Journal of quality in maintenance engineering, vol. 11, no. 4, pp. 348-358. https://doi.org/10.1108/13552510510626972
Dias G. (2017). Evolvement of computer aided software engineering (CASE) Tools: A user experience, International journal of computer science and software engineering, vol. 6, issue 3, pp. 55-60.
Vassilyeva S.N., Kelinab A.Yu., Kudinovb Y.I. and Pashchenkoa F.F. (2016). Intelligent control systems, XIIth International Symposium «Intelligent Systems» (INTELS'16), pp. 623-628.
Halonen R. and Paavilainen J. (2005). Hierarchical model of problems in omplementing information systems, Pacific Asia Conference on Information Systems, pp. 350-362.
Mironov V.V. and Yusupov N.I. (2000). Issledovanie ierarkhicheskoj situaczionnoj modeli s trekhznachnymi predikatami v ASU tekhnicheskimi obektami. Prinyatie reshenij v usloviyakh neopredelennosti [Investigation of a hierarchical situational model with three-valued predicates in ACS technical objects. Decision making under uncertainty], Bulletin of the Ufa State Aviation Technical University, pp. 7-15. (in Russian)
Butans J. and Orlovs I. (2016). Diagnostics and long term prognostics for investment decision support in smart Grids, IFAC-PapersOnLine, vol. 49, issue 28, pp. 13-18. https://doi.org/10.1016/j.ifacol.2016.11.003
