METHOD FOR CALCULATING THE SUFFICIENCY CRITERION OF DIAGNOSTIC INFORMATION FOR EXECUTING THE SELF-DIAGNOSIS ALGORITHM IN MULTI-MACHINE INFORMATION SYSTEMS
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Abstract
Subject, theme and main goal. When multi-machine system is working in autonomous mode, it’s critical to realize an algorithm of self-diagnosis of this system. There are many algorithms of self-diagnosis of multi-machine systems, and many of modern ones purpose a decoding of diagnostic information. But conditions, when we may start decoding of diagnostic information, are not studied enough good. These work purpose and justifies a new condition of this type, based on minimal volume of diagnostic information, realization of which may be interpreted as allow of start of decoding of diagnostic information. Methods. When diagnostic information accumulates, a general scheme of this accumulation may be described using ordered graph, which sometimes is called as diagnostic graph. If we know diagnostic graph of studied multi-machine information system, we may try to formulate some properties of self-diagnosis of the system. Using certain assumptions, some properties of volume of diagnostic information may be formulated too. Using assumptions like equivalent number of elementary checks, provided by every machine in the system, we may easily calculate a minimal volume of diagnostic information, the achievement of which guarantee, that every machine is checked at least one time. In this work some similar assumptions are used for calculation of minimal volume of diagnostic information, what potentially is enough for start decoding of diagnostic information. Results. In the work a a new condition for start of decoding of diagnostic information, what is enough for start of its decoding, is purposed and justified. Conclusions. The work purpose and justifies a minimal volume of diagnostic information, what is enough for start of its decoding, method of calculation of which is based on diagnostic graph of studied multi-machine system. A usage of this minimal volume is demonstrated on two examples with different diagnostic graphs. In other hand, some properties of this volume. As the result of number of simulations a range of this volume, depends on number of machines, was calculated.
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Winter, A., Ammenwerth, E., Haux, R., Marschollek, M., Steiner, B. and Jahn, F. (2023), Health information systems, Springer International Publishing, Cham, 259 p., doi: https://doi.org/10.1007/978-3-031-12310-8
Chihara, N., Takata, T., Fujiwara, Y., Noda, K., Toyoda, K., Higuchi, K. and Onizuka, M. (2023), “Effective detection of variable celestial objects using machine learning-based periodic analysis”, Astronomy and Computing, vol. 45, art. no. 100765, doi: https://doi.org/10.1016/j.ascom.2023.100765
Furusawa, H., Koike, M., Takata, T., Okura, Y., Miyatake, H., Lupton, R.H., Bickerton, S., Price, P.A., Bosch, J., Yasuda, N., Mineo, S., Yamada, Y., Miyazaki, S., Nakata, F., Koshida, S., Komiyama, Y., Utsumi, Y., Kawanomoto, S., Jeschke, E., Noumaru, J., Schubert, K., Iwata, I., Finet, F., Fujiyoshi, T., Tajitsu, A., Terai, T. and Lee, C.-H. (2018), “The on-site quality-assurance system for Hyper Suprime-Cam: OSQAH”, Publications of the Astronomical Society of Japan, vol. 70 (Special Is. 1), art. no. S3, doi: https://doi.org/10.1093/pasj/psx079
He, S. (2022), “Auto sales forecasting model based on target customer satisfaction theory”, 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022), Guangzhou, China, 28–30 October 2022, Paris, France, doi: https://doi.org/10.2991/978-94-6463-108-1_78
Mashkov, V.A. and Barabash, O.V. (1995), “Self-Checking of Modular Systems under Random Performance of Elementary Checks”, Engineering Simulation, vol. 12. pp. 433–445, available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-0029547320&origin=resultslist&sort=plf-f&src=s&sot=b&sdt=b&s=TITLE%28Self-Checking+of+Modular+Systems+under+Random+Performance+of+Elementary+Checks%29
Mashkov, O., Bychkov, A., Kalahnik, G., Shevchenko, V. and Vyshemyrska, S. (2022), “Application of the Theory of Functional Stability in the Problems of Covering Territories by Sensory Networks”, in Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making, 2022 Int. Scientific Conference "Intellectual Systems of Decision-Making and Problems of Computational Intelligence, Proc., Springer, pp. 266–285, doi: https://doi.org/10.1007/978-3-031-16203-9
Chumakevych, V., Pohrebennyk, V., Mashkov, O., Takosoglu, Ja. and Ptashnyk. V. (2020), “Structure Optimization of Functionally Sustainable Electromechanical Systems”, Przegląd Elektrotechniczny, Poland, R. 96 NR 10/2020, pр. 170–173, doi: https://doi.org/10.15199/48.2020.10.32
Aleksandrov, Y., Aleksandrova, T., Kostianyk, І. and Morgun, Y. (2023), “Selection of the Set of Allowable Values of the Variable Parameters of the Stabilizer of a Complex Dynamic Object”, Advanced Information Systems, vol. 7, no. 3, pp. 5–12, doi: https://doi.org/10.20998/2522-9052.2023.3.01
Mashkov, V.A. and Mashkov, O.A. (2015), “Interpretation of diagnosis problem of system level self-diagnosis”, Mathemetical Modeling and Computing, vol. 2(1), pp. 71–76, doi: https://doi.org/10.23939/mmc2015.01.071
Ntalampiras, S. (2015), “Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling”, IEEE Transactions on Neural Networks and Learning Systems, vol. 26 (9), art. no. 6932473, pp. 1939–1949, doi: https://doi.org/10.1109/TNNLS.2014.2362015
Kobayashi, M., Takabatake T., Matsushima, T. and Hirasawa, S. (2011), “Probabilistic fault diagnosis and its analysis in multicomputer systems”, Conference: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Anchorage, Alaska, USA, October 9-12 2011, doi: https://doi.org/10.1109/ICSMC.2011.6083862
Raskin, L., Karpenko, V., Ivanchykhin, Y., and Sokolov, D. (2023), “Diagnosis of Systems Under Conditions of Small Initial Data Sampling”, Advanced Information Systems, vol. 7, no. 3, pp. 39–43, doi: https://doi.org/10.20998/2522-9052.2023.3.05
Raskin, L., Sukhomlyn, L., Sokolov, D., Vlasenko, V. and Ntalampiras, S. (2018), “Fault diagnosis for smart grids in pragmatic conditions”, IEEE Transactions on Smart Grid, vol. 9 (3), pp. 1964–1971, doi: https://doi.org/10.1109/TSG.2016.2604120
Oliveira, D. F., Gomes, J., Pereira, R., Brito, M. and Machado, R.-J. (2022), “Development of a Self-Diagnostic System Integrated into a Cyber-Physical System”, Computers, vol. 11, 131, doi: https://doi.org/10.3390/computers11090131
Raskin, L., Sukhomlyn, L., Sokolov, D. and Vlasenko, V. (2023), “Multi-Criteria Evaluation of the Multifactor Stochastic Systems Effectiveness”, Advanced Information Systems, vol. 7, no. 2, pp. 63–67, doi: https://doi.org/10.20998/2522-9052.2023.2.09
Zhu, M., Li, J., Wang, W. and Chen, D. (2021), “Self-Detection and Self-Diagnosis Methods for Sensors in Intelligent Integrated Sensing System”, IEEE Sensors Journal, vol. 21, no. 17, art. no. 9461738, pp. 19247–19254, doi:. https://doi.org/10.1109/JSEN.2021.3090990
Mashkov, O., Chumakevych, V., Sokulsky, O. and Chyrun, L. (2019), “Features of determining controlling effects in functionally-stable systems with the recovery of a control”, Mathematical Modeling and Computing, vol. 6, no. 1, pp. 80–86, doi: https://doi.org/10.23939/mmc2019.01.085
Yudin, O., Mashkov, O., Kravchenko, Y., Gherniak, A., Salkutsan, S. and Tyshchenko, M. (2021), “Reconfiguration of computations in multiprocessor computing system”, 3rd IEEE International Conference on Advanced Trends in Information Theory, Proceedings, Kyiv, 15 December 2021 through 16 December 2021, code 176541, pp. 169–173, doi: https://doi.org/10.1109 / Atit 54053.2021.9678645
Kobayashi, M., Goto, M., Matsushima, T. and Hirasawa, S. (2014), “Robustness of syndrome analysis method in highly structured fault-diagnosis systems”, Conference Proceedings: IEEE International Conference on Systems, Man and Cybernetics, 2014-January, art. no. 6974345, pp. 2757–2763, doi: https://doi.org/10.1109/smc.2014.6974345
Mashkov, V.A. and Barabash, O.V. (1996), “Self-testing of multimodule systems based on optimal check-connection structures”, Engineering Simulation, vol. 13 (3), pp. 479–492, available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-0029752150&origin=resultslist&sort=plf-f&src=s&sot=b&sdt=b&s=TITLE%28Self-testing+of+multimodule+systems+based+on+optimal+check-connection+structures%29
Sobchuk, V., KalChuk, I., Kharkevych, G., Laptiev, O., Kharkevych, Y. and, Makarchuk, A. (2021), “Solving the Problem of Convergence of the Results of Analog Signals Conversion in the Process of Aircraft Control”, 2021 IEEE 6th International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD-2021, Proceedings, pp. 29–32, doi: https://doi.org/10.1109/APUAVD53804.2021.9615437
Rohret, D., Kraft, M. and Vella, M. (2013), “Functional Resilience, Functional Resonance and Threat Anticipation for Rapidly Developed Systems”, Journal of Information Warfare, vol. 12(2), pp. 50–62, available at: https://www.jstor.org/stable/26486855
Kovalenko, A. and Kuchuk, H. (2022), “Methods to Manage Data in Self-healing Systems”, Studies in Systems, Decision and Control, vol. 425, pp. 113–171, doi: https://doi.org/10.1007/978-3-030-96546-4_3
Bellini, E., Coconea, L. and Nesi P. (2020), “A Functional Resonance Analysis Method Driven Resilience Quantification for Socio-Technical Systems”, IEEE Systems Journal, vol. 14, no. 1, March 2020, pp. 1234–1244. doi: https://doi.org/10.1109/JSYST.2019.2905713