Methods of decision theory as a way to increase the reliability of assessment of the technical systems state
https://doi.org/10.25206/1813-8225-2025-196-82-91
EDN: GTXLIW
Abstract
The article discusses various approaches to technical diagnostics based on decision-making methods. Particular attention is paid to probabilistic and statistical methods that help to make informed decisions, taking into account the likelihood of certain functions being performed. The study shows that the choice of a specific technique depends on the specific problem being considered. Bayesian methods are efficient and easy to calculate, which makes them popular for industrial applications in assessing technical condition. For high-risk systems the article recommends to apply the Wald method.
Keywords
About the Authors
V. A. DyshlevskiyRussian Federation
Vyacheslav Aleksandrovich Dyshlevskiy, Postgraduate
Radio Engineering Devices and Diagnostic Systems Department
644050; Mira Ave., 11; 644010; Martynov Boul., 4; Omsk
AuthorID (RSCI): 1262001
I. S. Kudryavtseva
Russian Federation
Irina Sergeyevna Kudryavtseva, Candidate of Technical Sciences, Associate
Professor
Radio Engineering Devices and Diagnostic Systems Department
644050; Mira Ave., 11; Omsk
AuthorID (RSCI): 797354
A. P. Naumenko
Russian Federation
Aleksandr Petrovich Naumenko, Doctor of Technical Sciences, Professor, Professor of the Department
Radio Engineering Devices and Diagnostic Systems Department
644050; Mira Ave., 11; Omsk
AuthorID (RSCI): 243994
V. A. Rol’geyzer
Russian Federation
Varvara Andreyevna Rol’geyzer, Postgraduate
Radio Engineering Devices and Diagnostic Systems Department
644050; Mira Ave., 11; 644046; Karl Marks Ave., 35; Omsk
AuthorID (RSCI): 1287111
References
1. Kostyukov V. N., Naumenko A. P. Sistema kontrolya tekhnicheskogo sostoyaniya mashin vozvratno-postupatel’nogo deystviya [The monitoring system of a technical condition of machines of reciprocating action]. Kontrol’. Diagnostika. Journal of Russian Society for Non-Destructive Testing and Technical Diagnostics. 2007. No. 3. P. 50–58. EDN: HZEZYT. (In Russ.).
2. Kostyukov V. N., Naumenko A. P. Sistema monitoringa tekhnicheskogo sostoyaniya porshnevykh kompressorov neftepererabatyvayushchikh proizvodstv [Monitoring system upon the technicalcondition of piston compressors on oil refining manufactures]. Neftepererabotka i Neftekhimiya. Nauchno-tekhnicheskiye Dostizheniya i Peredovoy Opyt. 2006. No. 10. P. 38–47. EDN: HYODZL. (In Russ.).
3. Dyshlevskiy V. A. Prichiny otkazov mikrokontrollerov i sposoby povysheniya ikh bezotkaznosti [The causes of microcontroller failures and ways to improve their reliability]. Nauchnoye Soobshchestvo Studentov XXI Stoletiya. Tekhnicheskiye Nauki. Novosibirsk, 2021. P. 78–86. EDN: UWLQNY. (In Russ.).
4. Bolotin V. V. Resurs mashin i konstruktsiy [Resource of machines and structures]. Moscow, 1990. 447 p. ISBN 521-700-84-07. (In Russ.).
5. Bardyshev O. A., Popov V. A., Korovin S. K., Filin A. N. Monitoring tekhnicheskogo sostoyaniya tekhnicheskikh ustroystv na opasnykh proizvodstvennykh ob”yektakh [Monitoring of technical condition of technical devices at hazardous production facilities]. Bezopasnost’ truda v promyshlennosti. Occupational Safety in Industry. 2020. No. 1. P. 52–56. DOI: 10.24000/0409-2961-2020-1-52-56. EDN: MNYKJP. (In Russ.).
6. Kostyukov V. N., Naumenko A. P. Vliyaniye chelovecheskogo faktora na otsenku veroyatnosti propuska otkaza sistemoy monitoring [Human factor influence on the assessment of the monitoring system failure probability]. V mire nerazrushayushchego kontrolya. Industrial Safety. 2016. Vol. 19, no. 1. P. 72–77. EDN: VOFJJL. (In Russ.).
7. Kostyukov V. N., Boychenko S. N., Naumenko A. P. [et al.]. Riski monitoringa oborudovaniya toplivno-energeticheskogo kompleksa [Risks of monitoring fuel and energy complex equipment]. Novoye v rossiyskoy elektroenergetike. 2014. No. 3. P. 30–44. EDN: TEMIYH. (In Russ.).
8. Rassledovaniye avarii na TETs-3 AO «NTEK» zaversheno [The investigation of the accident at CHPP-3 of JSC NTEK has been completed]. NKPROM. URL: https://nkprom.ru/news/norilsk-rassledovanie-avarii-na-tets-3-ao-ntek-zaversheno/ (accessed: 01. 09. 2024). (In Russ.).
9. Resheniye Arbitrazhnogo suda Krasnoyarskogo kraya ot 12 fevralya 2021 g. po delu N A33-27273/2020 [Decision of the Arbitration Court of the Krasnoyarsk Territory dated February 12, 2021 in case No. A33-27273/2020]. URL: https://ohranatruda.ru/upload/medialibrary/1a2/Reshenie-po-delu-_-A33_27273_2020.pdf (accessed: 01. 09. 2024). (In Russ.).
10. Akt tekhnicheskogo rassledovaniya prichin avarii, proisshedshey 17 avgusta 2009 goda v filiale Otkrytogo Aktsionernogo Obshchestva «RusGidro» — «Sayano-Shushenskaya GES imeni P. S. Neporozhnego» [Act of technical investigation of the causes of the accident that occurred on August 17, 2009 in the branch of the Open Joint Stock Company “RusHydro — Sayano-Shushenskaya HPP named after P. S. Neporozhny”]. Moscow, 2009. 123 p. (In Russ.).
11. Matevosyan G. A., Dolgushina L. V. Prognozirovaniye obstanovki pri avariynykh situatsiyakh na Achinskom NPZ [Forecasting of emergency situations at the Achinsk Oil Refinery]. Monitoring, Modelirovaniye i Prognozirovaniye Opasnykh Prirodnykh Yavleniy i Chrezvychaynykh Situatsiy. Zheleznogorsk, 2020. P. 151–154. EDN: RRCGHM. (In Russ.).
12. GOST R 27.102–2021. Nadezhnost’ v tekhnike. Nadezhnost’ ob”yekta. Terminy i opredeleniya [Dependability in technics. Dependability of item. Terms and definitions]. Moscow, 2021. 40 p. (In Russ.).
13. Boldyrev A. S. Osnovnyye ponyatiya teorii prinyatiya resheniy [The basic concepts of decision theory]. Vestnik Sankt-Peterburgskogo universiteta MVD Rossii. Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia. 2013. Vol. 57, no. 1. P. 87–91. EDN: QBBEBL. (In Russ.).
14. Kuznetsov, Yu. V. Problemy teorii i praktiki menedzhmenta [Problems of theory and practice of management]. Saint-Petersburg, 1994. 206 p. (In Russ.).
15. Fountzoula C., Aravossis K. Decision-making methods in the public sector during 2010–2020 : A systematic review, advances in operations research. 2022. P. 1–13. DOI: 10.1155/2022/1750672.
16. Il’inskikh E. N., Filatova E. N., Samoylov K. V. [et al.]. Primeneniye algoritma dereva resheniy dlya ranney differentsial’noy diagnostiki mezhdu razlichnymi klinicheskimi formami ostrogo iksodovogo kleshchevogo borrelioza i kleshchevogo entsefalita [Applying decision tree algorithms to early differential diagnosis between different clinical forms of acute lyme borreliosis and tick-borne encephalitis]. Epidemiologiya i infektsionnyye bolezni. Epidemiology and Infectious Diseases. 2023. Vol. 28, no. 5. P. 275–288. DOI: 10.17816/EID601806. EDN: PBJRBT. (In Russ.).
17. Vafin R. R., Dinislamova R. R., Zulkarneyev R. Kh. Postroyeniye modeli prinyatiya vrachebnykh resheniy dlya differentsial’noy diagnostiki zabolevaniy v forme dereva resheniy [Creating a model of medical decision-making for differential diagnosis of diseases in the form of a decision tree]. Informatsionnyye Tekhnologii Intellektual’noy Podderzhki Prinyatiya Resheniy. Ufa, 2019. P. 255–260. EDN: AJERHR. (In Russ.).
18. Ahmed S. Evaluation of serum ferritin for prediction of severity and mortality in COVID-19-A cross sectional study. Annals of Medicine and Surgery. 2021. Vol. 63. P. 102–163. DOI: 10.1016/j.amsu.2021.02.009.
19. Pfob A., Lu S. C., Sidey-Gibbons C. Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. BMC Medical Research Methodology. 2022. Vol. 22, no. 1. P. 282. DOI: 10.1186/s12874-022-01758-8.
20. Romodanovskiy D. P., Khokhlov A. L. Vozmozhnost’ prognozirovaniya rezul’tatov issledovaniy bioekvivalentnosti na osnove posledovatel’nogo statisticheskogo analiza informativnykh faktorov [The ability to predict the results of bioequivalence studies based on a consistent statistical analysis of informative factors]. Kachestvennaya klinicheskaya praktika. Good Clinical Practice. 2020. No. 1. P. 80–98. DOI: 10.37489/2588-0519-2020-1-80-99. EDN: DTQTIU. (In Russ.).
21. McNamara T. P., Chen X. Bayesian decision theory and navigation. Psychonomic Bulletin and Review. 2022. Vol. 29, no. 3. P. 721–752. DOI: 10.3758/s13423-021-01988-9.
22. Perera L. P., Carvalho J. P, Guedes Soares C. Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions. Journal of Marine Science and Technology. 2011. Vol. 16. P. 84–99. DOI: 10.1007/s00773-010-0106-x.
23. Drovnikova I. G., Zolotykh E. S. Metodicheskiy podkhod k otsenke veroyatnosti realizatsii setevykh atak na ob”yektakh informatizatsii organov vnutrennikh del [Methodological approach to assessing the probability of network attacks on the objects of informatization of internal affairs bodies]. Vestnik Voronezhskogo instituta MVD Rossii. Vestnik of Voronezh Institute of the Ministry of the Interior of Russia. 2023. No. 4. P. 33–44. EDN: VONOPW. (In Russ.).
24. Khokhlova A. E., Baklanova O. E., Tezekpayeva Sh. T. Primeneniye metoda naivnogo Bayyesa pri reshenii zadachi fil’tratsii spama [Application of the naive bayes method in solving the spam filtering problem]. Vestnik VKTU. Bulletin of D. Serikbayev East Kazakhstan Technical University. 2024. No. 2. DOI: 10.51885/1561-4212_2024_2_210. (In Russ.).
25. Hazra T., Anjaria K. Applications of game theory in deep learning: a survey. Multimedia Tools and Applications. 2022. Vol. 81. P. 8963–8994. DOI: 10.1007/s11042-022-12153-2.
26. Naumenko A. P. Nauchno-metodicheskiye osnovy vibrodiagnosticheskogo monitoringa porshnevykh mashin v real’nom vremeni [Scientific and methodological foundations of vibration diagnostic monitoring of reciprocating machines in real time]. Omsk, 2012. 423 p. (In Russ.).
27. Kostyukov V. N., Naumenko A. P. Osnovy vibroakusticheskoy diagnostiki i monitoringa mashin [Fundamentals of vibroacoustic diagnostics and monitoring of machines]. Omsk, 2011. 360 p. ISBN 978-5-8149-1101-8. EDN: QMHHFH. (In Russ.).
28. Orlov A. I. Osnovnyye idei kontrollinga statisticheskikh metodov [The main ideas of statistical methods controlling]. Politematicheskiy setevoy elektronnyy nauchnyy zhurnal Kubanskogo gosudarstvennogo agrarnogo universiteta. Polythematic Online Scientific Journal of Kuban State Agrarian University. 2025. No. 210. P. 317–347. DOI: 10.21515/1990-4665-210-033. EDN: TBBTIC. (In Russ.).
29. Kudryavtseva I. S., Naumenko A. P., Dyshlevskiy V. A., Rol’geyzer V. A. Veroyatnostno-statisticheskiye metody prinyatiya resheniy: primeneniye ROC-analiza v tekhnicheskoy diagnostike []. Kontrol’. Diagnostika. Journal of Russian Society for Non-Destructive Testing and Technical Diagnostics. 2025. Vol. 28. No. 8 (326). P. 4–17. DOI: 10.14489/td.2025.08.pp.004-017. EDN: REQULO. (In Russ.).
30. Kudryavtseva I. S., Naumenko A. P., Demin A. M., Odinets A. I. Veroyatnostno-statisticheskiy kriteriy otsenki sostoyaniya po parametram vibroakusticheskogo signala [Probabilistic and statistical criterions for assessing the condition by vibroacoustic signal parameters]. Dinamika sistem, mekhanizmov i mashin. Dynamics of Systems, Mechanisms and Machines. 2019. Vol. 7, no. 2. P. 113–122. DOI: 10.25206/2310-9793-7-2-113-122. EDN: EYLIAI. (In Russ.).
31. Demin A. M., Naumenko A. P., Odinets A. I., Gorchakova A. A. Otsenka veroyatnostnykh oshibok kontrolya sostoyaniya teploobmennogo oborudovaniya [Probability mistakes evaluation of the heat-exchange equipment condition monitoring]. Dinamika sistem, mekhanizmov i mashin. Dynamics of Systems, Mechanisms and Machines. 2019. Vol. 7, no. 2. P. 95–103. DOI: 10.25206/2310-9793-7-2-95-103. EDN: KJPDXY. (In Russ.).
32. Blyagoz Z. U., Popova A. Yu. Prinyatiye resheniy v usloviyakh riska i neopredelennosti [Decision-making in conditions of risk and uncertainty]. Vestnik Adygeyskogo Gosudarstvennogo Universiteta. 2006. No. 4. P. 164–168. EDN: KAOVEZ. (In Russ.).
33. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters. 2006. Vol. 27, № 8. P. 861–874. DOI: 10.1016/j.patrec.2005.10.010.
34. Stehman S. V. Selecting and interpreting measures of thematicclassification accuracy. Remote Sensing of Environment. 1997. Vol. 62, № 1. P. 77–89. DOI: 10.1016/S0034-4257(97)00083-7.
35. Narayanan V., Arora I., Bhatia A. Fast and Accurate sentiment classification using an enhanced Naive Bayes Model. Lecture Notes in Computer Science. 2013. P. 194–201. DOI: 10.1007/978-3-642-41278-3_24.
36. Wu X., Kumar V., Quinlan J. R., Ghosh J. [et al.]. Top 10 algorithms in data mining. Knowledge and Information Systems. 2007. Vol. 14, № 1. P. 1–37. DOI: 10.1007/s10115-007-0114-2.
37. Malla C., Panigrahi I. Review of condition monitoring of rolling element bearing using vibration analysis and other techniques. Journal of Vibration Engineering and Technologies. 2019. Vol. 7. P. 407–414. DOI: 10.1007/s42417-019-00119-y.
38. Kostyukov V. N. Razrabotka elementov teorii, tekhnologii i oborudovaniya sistem monitoringa agregatov neftekhimicheskikh kompleksov [Development of elements of theory, technology and equipment of monitoring systems for aggregates of petrochemical complexes]. Moscow, 2001. 21 p. (In Russ.).
39. Petrukhin V. V., Petrukhin S. V. Vibratsiya, vibrodiagnostika i … ETsN [Vibration, vibration diagnostics and ... EDCP]. Moscow, 2022. 186 p. ISBN 978-5-466-01822-6. EDN: NKDSWO. (In Russ.).
40. Dmitriev S. A., Khalyasmaa A. I. Power equipment technical state assessment principles. Applied Mechanics and Materials. 2014. Vol. 492. P. 531–535. DOI: 10.4028/www.scientific.net/AMM.492.531.
41. Kirdishchev D. V. Primeneniye metoda Bayyesa pri vyyavlenii defektov toplivnoy apparatury po vibroakusticheskim kharakteristikam vo vremya raboty dizelya [Application of the bayes method for detecting defects in fuel equipment by vibroacoustic characteristics during the operation of a diesel engine]. Vestnik Gomel’skogo gosudarstvennogo tekhnicheskogo universiteta im. P. O. Sukhogo. Bulletin of Sukhoi State Technical University of Gomel. 2021. Vol. 4, no. 1. P. 92–99. EDN: MMINUO. (In Russ.).
42. Gubarev P. V., Shapshal A. S., Shabayev V. V. Raspoznavaniye diagnozov neispravnosti tekhnicheskoy sistemy [Recognition of fault diagnoses technical system]. Izvestiya Tul’skogo gosudarstvennogo universiteta. Tekhnicheskiye nauki. News of the Tula State University. Technical Sciences. 2020. No. 10. P. 322–326. EDN: OPXBXB. (In Russ.).
43. Gruntovich N. V., Kirdishchev D. V. Primeneniye metoda Val’da pri vyyavlenii defektov toplivnykh forsunok dizelya. Problemy Energoobespecheniya, Avtomatizatsii, Informatizatsii i Prirodopol’zovaniya v APK. Bryansk, 2023. P. 99–105. EDN: VHKJTG. (In Russ.).
44. Chokoy V. Z. Instrumenty prinyatiya diagnosticheskikh resheniy po ob”yektam aviatekhniki v usloviyakh neopredelлnnosti i riska [Tools of non-parametric estimate of aircraft equipment reliability with censored data]. Crede Experto: transport, obshchestvo, obrazovaniye, yazyk. Crede Experto: Transport, Society, Education, Language. 2016. No. 2. P. 20–31. EDN: WAQOVB. (In Russ.).
45. Yamaliyev V. U., Mamalimova I. R. Statisticheskiy podkhod pri otsenke tekhnicheskogo sostoyaniya porodorazrushayushchego instrumenta [Statistical approach to evaluating the technical condition of the breeding destructing instrument]. Materialy 47-y Vseros. Nauch.-Tekhn. Konf. Molodykh Uchenykh, Aspirantov i Studentov s Mezhdunar. Uchastiyem. Oktabrskiy, 2020. P. 712–715. EDN: OAYHED. (In Russ.).
46. Marasanov V. V., Sharko A. O., Sharko O. V. Sistemnaya klassifikatsiya kriteriyev otsenki tekhnicheskogo sostoyaniya ob”yektov po istochnikam akusticheskikh signalov [System classification criteria of estimation the technical state of objects on sources of acoustic signals]. Vestnik Khersonskogo natsional’nogo tekhnicheskogo universiteta. Visnyk of Kherson National Technical University. 2016. Vol. 56, no. 1. P. 51–60. EDN: WBKHVD. (In Russ.).
Review
For citations:
Dyshlevskiy VA, Kudryavtseva IS, Naumenko AP, Rol’geyzer VA. Methods of decision theory as a way to increase the reliability of assessment of the technical systems state. Omsk Scientific Bulletin. 2025;(4):82-91. (In Russ.) https://doi.org/10.25206/1813-8225-2025-196-82-91. EDN: GTXLIW
JATS XML





















