https://doi.org/10.62660/bcstu/4.2025.38

Volume 30, No. 4, 2025

38-51

  • Read article

    • Automated error logging in the flowmeter design process: Approaches to processing and analysis

    Rostyslav Sapeliuk Vitalii Roman

    Received 14.07.2025, Revised 07.11.2025, Accepted 15.12.2025

    Abstract

    In the modern design of variable differential pressure flowmeters, the introduction of reliable automated logging systems is relevant, as conventional logging methods do not provide the required accuracy and stability under load. The purpose of this study was to substantiate and develop methodological approaches to automating logging processes in the design of variable differential pressure flowmeters, considering parametric optimisation, reducing error localisation time, and increasing the accuracy of uncertainty estimation. The study was based on experimental measurements in the SolidWorks 2024 and ANSYS Fluent software environments using the Elasticsearch and Kibana tools, as well as further computational processing in MATLAB 2024a. The evaluation covered the metrics of accuracy, completeness, integrated harmonic mean, area under the performance curve, time to detect a critical event, time to notify an engineer, time to localise an error, average error in flow calculation with bootstrap analysis, and an integrated logging efficiency index. The study found that basic logging provides limited accuracy (≈ 71%) and low stability (≈ 82.5% of failure-free sessions), while heuristic methods increase efficiency to 87.9%, but leave a considerable level of event duplication and lose stability under load. The statistical classification showed better results (integrated F1-score = 0.81, average consumption error = 2.5%, integrated logging efficiency index = 0.78), providing a balance between accuracy and performance. The highest indicators were achieved with the machine learning approach: accuracy exceeded 91%, completeness was over 87%, the average cost calculation error was reduced to 1.7%, the recovery of cause-and-effect relationships reached over 86%, and the integrated logging efficiency index was 0.89. Analysis of variance and the non-parametric Kruskal-Wallis test confirmed the reliability of the differences between the approaches. The practical significance of this study lies in the identification of machine learning algorithms as a basic direction for the development of intelligent logging systems, the findings of which can be used by engineering companies, software developers, and enterprises in the oil and gas, energy, and mechanical engineering industries to improve the reliability, scalability, and adaptability of design systems to real-world operating conditions

    Keywords:

    numerical fluid dynamics; machine learning; error diagnostics; integral logging efficiency index; variable differential pressure flowmeters

    Suggested citation
    Sapeliuk, R., & Roman, V. (2025). Automated error logging in the flowmeter design process: Approaches to processing and analysis. Bulletin of Cherkasy State Technological University, 30(4), 38-51. https://doi.org/10.62660/bcstu/4.2025.38
    405 Views

    References

    1. Al-Kadem, M., Gomaa, M., Al Yateem, K., & Al Maghlouth, A. (2022). Multiphase flowmeter health monitoring strategy: Maximizing the value of real-time sensors and automation for industrial revolution 4.0. SPE Production & Operations, 37(3), 533-542. doi: 10.2118/206281-PA.
    2. Ansys. (n.d.). Ansys Fluent: Fluid simulation software. Retrieved from https://www.ansys.com/products/fluids/ ansys-fluent.
    3. Camperos, J.A.G., Cely, M.M.H., & García, A.P. (2024). Artificial intelligence techniques for the hydrodynamic characterization of two-phase liquid-gas flows: An overview and bibliometric analysis. Fluids, 9(7), article number 158. doi: 10.3390/fluids9070158.
    4. Cândido, J., Aniche, M., & van Deursen, A. (2021b). Log-based software monitoring: A systematic mapping study. PeerJ Computer Science, 7, article number e489. doi: 10.7717/peerj-cs.489.
    5. Cândido, J., Haesen, J., Aniche, M., & van Deursen, A. (2021a). An exploratory study of log placement recommendation in an enterprise system. In 2021 IEEE/ACM 18th international conference on mining software repositories (MSR) (pp. 143-154). Madrid: IEEE. doi: 10.1109/MSR52588.2021.00027.
    6. Castellanos, R., Maceda, G.Y.C., De La Fuente, I., Noack, B.R., Ianiro, A., & Discetti, S. (2022). Machine-learning flow control with few sensor feedback and measurement noise. Physics of Fluids, 34(4), article number 047118. doi: 10.1063/5.0087208.
    7. Chen, J., Hou, Z.-Y., Li, B., & Wang, S.-C. (2022). Vortex signal model based Kalman filter of vortex signal processing method. Review of Scientific Instruments, 93(4), article number 045004. doi: 10.1063/5.0072675.
    8. Chen, Z., Zhao, W., Shen, P., Wang, C., & Jiang, Y. (2024). A fault diagnosis method for ultrasonic flow meters based on KPCA-CLSSA-SVM. Processes, 12(4), article number 809. doi: 10.3390/pr12040809.
    9. Deng, L., Ambade, A., Hernandez de la Bastida, M., Davalos, D., Zanafria, J.G., & Gupta, S. (2022). Real-time electrical submersible pump smart alarms suite enabled through data analytics and edge-based virtual flowmeter. In SPE annual technical conference and exhibition (article number SPE-209958-MS). Houston: Society of Petroleum Engineers. doi: 10.2118/209958-MS.
    10. Dobrowolski, W., Nikodem, M., & Unold, O. (2023). Software failure log analysis for engineers – review. Electronics, 12(10), article number 2260. doi: 10.3390/electronics12102260.
    11. Elastic. (n.d.a). Open source search, analytics, and AI platform: Elasticsearch. Retrieved from https://www.elastic. co/elasticsearch.
    12. Elastic. (n.d.b). Kibana: Discover, iterate, and resolve with ES|QL on Kibana. Retrieved from https://www.elastic.co/ kibana.
    13. Gholamian, S., & Ward, P.A.S. (2021). A comprehensive survey of logging in software: From logging statements automation to log mining and analysis. ArXiv. doi: 10.48550/arXiv.2110.12489.
    14. Gilbert, C.D.,Vinoth, B., Srinivasulu, R.U., Uma, G., & Umapathy, M. (2022). Recurrent neural network based soft sensor for flow estimation in liquid rocket engine injector calibration. Flow Measurement and Instrumentation, 83, article number 102105. doi: 10.1016/j.flowmeasinst.2021.102105.
    15. He, S., He, P., Chen, Z., Yang, T., Su, Y., & Lyu, M.R. (2021). A survey on automated log analysis for reliability engineering. ACM Computing Surveys, 54(6), article number 130. doi: 10.1145/3460345.
    16. ISO/IEC 25002:2024. (2024). Systems and software engineering – systems and software Quality requirements and evaluation (SQuaRE) – suality model overview and usage. Retrieved from https://www.iso.org/standard/78175.html.
    17. ISO/IEC/IEEE 29119-1:2022. (2022). Software and systems engineering – software testing. Part 1: General concepts. Retrieved from https://www.iso.org/standard/81291.html.
    18. Keyogeg, B., Thompson, M., Dawson, G., Wagner, D., Johnson, G., & Elliott, B. (2024). Automated detection of ransomware in Windows Active Directory domain services using log analysis and machine learning. Authorea. doi: 10.22541/au.172779663.36925703/v1.
    19. Korzeniowski, Ł., & Goczyła, K. (2022). Landscape of automated log analysis: A systematic literature review and mapping study. IEEE Access, 10, 21892-21913. doi: 10.1109/ACCESS.2022.3152549.
    20. Li, C., Sun, L., Liu, J., Zhang, Y., Li, H., & Wang, H. (2021). Improvement of signal processing in Coriolis mass flowmeters for gas-liquid two-phase flow. Frontiers of Information Technology & Electronic Engineering, 22(2), 272-286. doi: 10.1631/FITEE.1900558.
    21. Ma, J., Xu, K.-J., Jiang, Z., Zhang, L., & Xu, H.-R. (2021). Applications of digital signal processing methods in TOF calculation of ultrasonic gas flowmeter. Flow Measurement and Instrumentation, 79, article number 101932. doi: 10.1016/j.flowmeasinst.2021.101932.
    22. Mohindru, P. (2023). Recent advancements in volumetric flow meter for industrial application. Heat and Mass Transfer, 59(11), 2149-2166. doi: 10.1007/s00231-023-03413-4.
    23. Moon, Y., Han, S.-T., Lee, J., & Mun, D. (2023). Extraction of line objects from piping and instrumentation diagrams using an improved continuous line detection algorithm. Journal of Mechanical Science and Technology, 37(4), 1959-1972. doi: 10.1007/s12206-023-0333-9.
    24. Paliwal, S., Jain, A., Sharma, M., & Vig, L. (2021). Digitize-PID: Automatic digitization of piping and instrumentation diagrams. In M. Gupta & G. Ramakrishnan (Eds.), PAKDD 2021 workshops: Trends and applications in knowledge discovery and data mining (pp. 168-180). Cham: Springer. doi: 10.1007/978-3-030-75015-2_17.
    25. Qu, J., Xue, Q., Wang, J., Sun, J., & Li, J. (2023). Optimization of a turbine flow well logging tool based on the response surface method. Machines, 11(4), article number 455. doi: 10.3390/machines11040455.
    26. R2024a release highlights – MATLAB and simulink. (n.d.). Retrieved from https://ch.mathworks.com/products/ new_products/release2024a.html.
    27. Ren, R., Wang, H., Sun, X., & Quan, H. (2022). Design and implementation of an ultrasonic flowmeter based on the cross-correlation method. Sensors, 22(19), article number 7470. doi: 10.3390/s22197470.
    28. Roman, V.I., Matiko, F.D., & Ilyuchok, V.O. (2024). Computer program for automated design of diametrical ultrasonic flowmeters. Scientific Works of Vinnytsia National Technical University, 2. doi: 10.31649/2307-53762024-2-54-63.
    29. Romeo, R., Postrioti, L., Torchio, D., Martino, M., & Malengo, A. (2025). Dynamic calibration of flow meters using reference flow rate profiles generated by injectors. Measurement, 256(A), article number 118129. doi: 10.1016/j. measurement.2025.118129.
    30. Roy, A., Kasiviswanathan, K.S., Patidar, S., Adeloye, A.J., Soundharajan, B.S., & Ojha, C.S.P. (2023). A novel physics‐aware machine learning‐based dynamic error correction model for improving streamflow forecast accuracy. Water Resources Research, 59(2), article number e2022WR033318. doi: 10.1029/2022WR033318.
    31. Sapeliuk, R., & Matiko, F. (2025). Analysis of the current state and development trends of computer-aided design systems for flow measurement instruments of fluid media. Open MenuBulletin of Kyiv Polytechnic Institute. Series Instrument Making, 69(1), 59-69. doi: 10.20535/1970.69(1).2025.331881.
    32. Shajarian, S. (2025). Towards autonomous network management: AI-driven framework for intelligent log analysis, troubleshooting and documentation. Proceedings of the AAAI Conference on Artificial Intelligence, 39(28), 29297-29298. doi: 10.1609/aaai.v39i28.35226.
    33. Shunashu, I.L., & Kaunde, O. (2025). Modelling over-reading correction factors for ultrasonic flow meters in wet gas measurement using advanced regression and machine learning techniques. SSRN. doi: 10.2139/ ssrn.5220797.
    34. Siqueira de Aquino, G., Martins, R.S., Martins, M.F., & Ramos, R. (2025). An overview of computational fluid dynamics as a tool to support ultrasonic flow measurements. Metrology, 5(1), article number 11. doi: 10.3390/ metrology5010011.
    35. SOLIDWORKS. (n.d.). Retrieved from https://solidworks.softico.ua/.
    36. Vicente, M., Guarda, J., & Batista, F. (2022). Gutenbrain: An architecture for equipment technical attributes extraction from piping & instrumentation diagrams. In KDIR 2022 – 14th international conference on knowledge discovery and information retrieval (pp. 204-211). Valletta: Science and Technology Publications.
    37. Wang, S., Ge, L., Tian, G., Wei, G., Xiao, X., & Zou, M. (2025). Research progress on optimization techniques for electromagnetic flowmeters: A review. IEEE Sensors Journal, 25(9), 14557-14574. doi: 10.1109/ JSEN.2025.3552894.
    38. Yu, M.-G., & Kim, D.-S. (2025). Low-complexity ultrasonic flowmeter signal processor using peak detectorbased envelope detection. Journal of Sensor and Actuator Networks, 14(1), article number 12. doi: 10.3390/ jsan14010012.
    39. Zhao, N., et al. (2021). An empirical investigation of practical log anomaly detection for online service systems. In Proceedings of the 29th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering (pp. 1404-1415). New York: Association for Computing Machinery. doi: 10.1145/3468264.3473933.