Diagnosis of the technical condition of high-tech complexes by probabilistic methods

• Autorzy: Budashko V. , Sandler A. , Shevchenko V.

Identyfikatory

DOI

10.12716/1001.16.01.11

• Języki publikacji: EN

Abstrakty

EN

When designing multilevel systems for monitoring the parameters and characteristics of high-tech complexes, it is necessary to effectively control the state of the elements of generating units. Existing control systems for their specification and technical characteristics do not fully meet the monitoring objectives. The capabilities of existing known systems have limitations on the depth of use and compensation for the impact of operational factors. The article proposes and substantiates the feasibility of using the principle of automated measurement and control of load in high-tech complexes based on the probabilistic approach. It is established that the presence of errors in the means of measurement and control leads to specific errors that should be taken when assessing the quality of control, solving management and control tasks. To register the transition of parameters beyond the limit values, a new sensor circuitry based on fiber-optic elements is proposed. The main difference of the proposed diagnostic tool is the invariance to operational destabilizing factors.

Słowa kluczowe

EN

probabilistic method; diagnosis; high-tech complexes; technical condition; automated measurement and control; diagnostic tool

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2022
• Tom: Vol. 16 No. 1
• Strony: 105--111
• Opis fizyczny: Bibliogr. 34 poz., rys.

Twórcy

autor

Budashko V.

• National University “Odessa Maritime Academy”, Odessa, Ukraine

autor

Sandler A.

• National University “Odessa Maritime Academy”, Odessa, Ukraine

autor

Shevchenko V.

• National University “Odessa Maritime Academy”, Odessa, Ukraine

Bibliografia

• 1. Akand, T. et al.: Low loss hollow-core optical fibers conjoining tube lattice and revolver structures. Results in Optics. 1, 100008 (2020).https://doi.org/10.1016/j.rio.2020.100008.
• 2. Akrofi, J.D. et al.: Multi-stack insulator to minimise threshold voltage drift in ZnO FET sensors operating in ionic solutions. Micro and Nano Engineering. 8, 100066 (2020). https://doi.org/10.1016/j.mne.2020.100066.
• 3. Allam, A. et al.: Linear Thermal Expansion Coefficients of Higher Manganese Silicide Compounds. Physics Procedia. 55, 24–29 (2014). https://doi.org/10.1016/j.phpro.2014.07.004.
• 4. Barde, M., Barde, P.: What to use to express the variability of data: Standard deviation or standard error of mean? Perspect Clin Res. 3, 3, 113–116 (2012). https://doi.org/10.4103/2229-3485.100662.
• 5. Bourne, M.: Table of Laplace Transforms, https://www.intmath.com/laplace-transformation/tablelaplace-transforms.php, last accessed 2022/03/30.
• 6. Budashko, V. et al.: Conceptualization of research of power hybrid electric power complexes. TAPR. 5, 1(31), 63–73 (2016). https://doi.org/10.15587/2312-8372.2016.81407.
• 7. Budashko, V.: Design of the Three-Level Multicriterial Strategy of Hybrid Marine Power Plant Control for a Combined Propulsion Complex. Electrical engineering & electromechanics. 2, 62–72 (2017).https://doi.org/10.20998/2074-272X.2017.2.10.
• 8. Budashko, V.: Formalization of design for physical model of the azimuth thruster with two degrees of freedom by computational fluid dynamics methods. EEJET. 3, 7 (87), 40–49 (2017). https://doi.org/10.15587/1729-4061.2017.101298.
• 9. Budashko, V.: Theoretical-Applied Aspects of the Composition of Regression Models for Combined Propulsion Complexes Based on Data of Experimental Research. Eastern-European Journal of Enterprise Technologies. 3, 88, 11–20 (2017). https://doi.org/10.15587/1729-4061.2017.107244.
• 10. Budashko, V., Shevchenko, V.: Solving a Task of Coordinated Control Over a Ship Automated Electric Power System under a Changing Load. Eastern- European Journal of Enterprise Technologies. 2, 110, 54–70 (2021). https://doi.org/10.15587/1729- 4061.2021.229033.
• 11. Budashko, V., Shevchenko, V.: The Synthesis of Control System to Synchronize Ship Generator Assemblies.Eastern-European Journal of Enterprise Technologies. 2,109, 45–63 (2021). https://doi.org/10.15587/1729-4061.2021.225517.
• 12. Budashko, V.V.: Increasing Control’s Efficiency for the Ship’s Two-mass Electric Drive. Electrical Engineering & Electromechanics. 0, 4, 34–42 (2016).https://doi.org/10.20998/2074-272X.2016.4.05.
• 13. Chen, B., Luo, X.-L.: Novel multivariate q-sigma rule focusing on process variation for incipient faultdetection in dynamic processes. Chemometrics and Intelligent Laboratory Systems. 206, 104149 (2020). https://doi.org/10.1016/j.chemolab.2020.104149.
• 14. Dunn, W.L., Shultis, J.K. eds: Exploring Monte Carlo Methods. Elsevier, Amsterdam (2012). https://doi.org/10.1016/B978-0-444-51575-9.00017-8.
• 15. Dvulit, P. et al.: Accuracy estimation of site coordinates derived from GNSS-observations by non-classical error theory of measurements. Geodesy and Geodynamics. 12, 5, 347–355 (2021).https://doi.org/10.1016/j.geog.2021.07.005.
• 16. Fonseca, T. et al.: Assessing innovation in transport: An application of the Technology Adoption (TechAdo) model to Maritime Autonomous Surface Ships (MASS). Transport Policy. 114, 182–195 (2021). https://doi.org/10.1016/j.tranpol.2021.09.005.
• 17. den Hollander, F. et al.: Law of large numbers for nonelliptic random walks in dynamic random environments. Stochastic Processes and their Applications. 123, 1, 156–190 (2013). https://doi.org/10.1016/j.spa.2012.09.002.
• 18. Holmberg, K. et al.: Topography orientation effects on friction and wear in sliding DLC and steel contacts, part 3: Experiments under dry and lubricated conditions. Wear. 486–487, 204093 (2021). https://doi.org/10.1016/j.wear.2021.204093.
• 19. I. Hvozdeva et al.: Problems of Improving the Diagnostic Systems of Marine Diesel Generator Sets. In: 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). pp. 350–354 (2020). https://doi.org/10.1109/TCSET49122.2020.235453.
• 20. Kakinuma, Y. et al.: External Sensor-less On-machine Measurement of Workpiece for Ultra-precision Machine Tools. Procedia CIRP. 9, 40–43 (2013). https://doi.org/10.1016/j.procir.2013.06.165.
• 21. Khonsari, M.M. et al.: On the running-in nature of metallic tribo-components: A review. Wear. 474–475, 203871 (2021). https://doi.org/10.1016/j.wear.2021.203871.
• 22. Krčum, M. et al.: Shipboard Monitoring and Control System. IFAC Proceedings Volumes. 30, 22, 165–169 (1997). https://doi.org/10.1016/S1474-6670(17)46508-6.
• 23. Lee, C., Kim, J.: Energy Efficient Control for Electric Ship Propulsion Considering Thrust Fluctuation in Regular Waves. IFAC-PapersOnLine. 54, 16, 364–369 (2021). https://doi.org/10.1016/j.ifacol.2021.10.117.
• 24. Mehnert, M. et al.: A complete thermo–electro–viscoelastic characterization of dielectric elastomers, Part I: Experimental investigations. Journal of the Mechanics and Physics of Solids. 157, 104603 (2021). https://doi.org/10.1016/j.jmps.2021.104603.
• 25. Rethfeldt, C. et al.: System Approach for Highly Automated Manoeuvring with Research Vessel DENEB. IFAC-PapersOnLine. 54, 16, 153–160 (2021). https://doi.org/10.1016/j.ifacol.2021.10.087.
• 26. Tang, R. et al.: Optimal operation of hybrid energy system for intelligent ship: An ultrahigh-dimensional model and control method. Energy. 211, 119077 (2020). https://doi.org/10.1016/j.energy.2020.119077.
• 27. Tillmann, A.M., Kobbelt, L.: Structured discrete shape approximation: Theoretical complexity and practical algorithm. Computational Geometry. 99, 101795 (2021). https://doi.org/10.1016/j.comgeo.2021.101795.
• 28. V. Budashko: Thrusters Physical Model Formalization with regard to Situational and Identification Factors of Motion Modes. In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). pp. 1–6 (2020). https://doi.org/10.1109/ICECCE49384.2020.9179301.
• 29. V. Myrhorod et al.: Multi-parameter Diagnostic Model of the Technical Conditions Changes of Ship Diesel Generator Sets. In: 2020 IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP). pp. 1–4 (2020). https://doi.org/10.1109/PAEP49887.2020.9240905.
• 30. Wang, P. et al.: Effects of bipolar repetitive square wave voltage parameters on electrical tree characteristics of epoxy resin. Polymer Testing. 103, 107371 (2021). https://doi.org/10.1016/j.polymertesting.2021.107371.
• 31. Wang, S. et al.: High-precision fiber optic liquid level sensor based on fast Fourier amplitude demodulation in a specific range of spectrum. Measurement. 187, 110326 (2022). https://doi.org/10.1016/j.measurement.2021.110326.
• 32. Wang, X. et al.: Random fiber laser based on an artificially controlled backscattering Erbium-Doped fiber. Optical Fiber Technology. 54, 102125 (2020). https://doi.org/10.1016/j.yofte.2019.102125.
• 33. Wang, Y.-Y. et al.: Real-time monitoring of pressure and temperature of oil well using a carbon-coated and bellow-packaged optical fiber sensor. Optical Fiber Technology. 67, 102703 (2021). https://doi.org/10.1016/j.yofte.2021.102703.
• 34. Zhang, M. et al.: A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions. Reliability Engineering & System Safety. 213, 107674 (2021). https://doi.org/10.1016/j.ress.2021.107674.