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Abstract

This article investigates various methods and tools for self-monitoring and fault tolerance in flow measurement transducers used in industrial processes. The study focuses on key techniques such as the use of redundancy, generation of reference values, analysis of measurement signals, and control of disturbance variables. These methods allow transducers to detect potential faults, ensure reliable operation, and maintain measurement accuracy even under adverse conditions. The article highlights how self-monitoring contributes to improving system safety, increasing reliability, and reducing downtime. It also discusses the integration of intelligent monitoring systems that support predictive maintenance and real-time diagnostics. Fault-tolerant sensors with self-monitoring capabilities can significantly reduce operational risks, minimize maintenance costs, and enhance overall efficiency in industrial environments. The paper outlines recent developments and trends in smart sensor technologies and emphasizes their practical applications in modern automated systems. By enabling early detection of errors and reducing dependency on external supervision, these advanced sensors represent a crucial step toward the automation and digitalization of industrial process control.

First Page

50

Last Page

55

References

  1. Baranovskiy, V. M., & Kudryavtsev, V. V. (2015). Izmeritel’nye preobrazovateli: printsipy raboty i primeneniya [Measuring Transducers: Principles of Operation and Applications]. Moscow: Mashinostroenie.
  2. Maslov, K. V. (2017). Avtomatizatsiya izmerenii i kontrol’ protsessov v promyshlennosti [Automation of Measurements and Process Control in Industry]. St. Petersburg: Piter.
  3. Schneider, J., et al. (2016). Sensor self-diagnostics in industrial applications: Principles and methods. IEEE Transactions on Instrumentation and Measurement, 65(5).
  4. Tortora, P., et al. (2018). Redundancy and self-calibration techniques in flow measurement systems. Flow Measurement and Instrumentation, 22(4).
  5. Lenk, A. V. (2019). Intellektual’nye datchiki v promyshlennosti [Intelligent Sensors in Industry]. Moscow: Tekhnosfera.
  6. Smith, R., & Johnson, P. (2020). Advances in Coriolis flow metering: Reliability and self-monitoring. Journal of Process Control, 30(2).
  7. Bachinskiy, I. V. (2021). Sovremennye metody diagnostiki i monitoringa datchikov v promyshlennykh sistemakh [Modern Methods of Diagnostics and Monitoring of Sensors in Industrial Systems]. Vestnik mashinostroeniya, (8).
  8. Rakhimov, A. R. (2015). Metody i sredstva kontrolya tekhnologicheskikh protsessov [Methods and Means of Control of Technological Processes]. Tashkent: Universitet.
  9. Turaev, Sh. I. (2019). Integratsiya intellektual’nykh datchikov v avtomatizirovannye sistemy upravleniya [Integration of Intelligent Sensors into Automated Control Systems]. Tashkent: Fan.
  10. Narmatov, U. K. (2017). Avtomatizatsiya tekhnologicheskikh protsessov i izmerenii v usloviyakh agressivnykh sred [Automation of Technological Processes and Measurements in Aggressive Environments]. Tashkent: TIIMSKh.
  11. Yusupov, I. K. (2020). Razrabotka i primenenie sistem samokontrolya v izmeritel’nykh preobrazovatelyakh davleniya [Development and Application of Self-Monitoring Systems in Pressure Measuring Transducers]. Vestnik NAN RUz, (3).
  12. Karimov, Kh. B. (2018). Sovremennye metody povysheniya nadezhnosti datchikov raskhoda v agressivnykh sredakh [Modern Methods to Improve the Reliability of Flow Sensors in Aggressive Environments]. Tashkent: Institut energetiki i avtomatiki.
  13. Saidov, B. Sh. (2019). Avtomatizatsiya i kontrol’ tekhnologicheskikh protsessov s ispol’zovaniem intellektual’nykh datchikov [Automation and Control of Technological Processes Using Intelligent Sensors]. Tashkent: TashGTU.
  14. Abdurakhmanov, Zh. S. (2021). Povyshenie tochnosti izmerenii v datchikakh raskhoda cherez ispol’zovanie algoritmov samokontrolya [Improving Measurement Accuracy in Flow Sensors Using Self-Monitoring Algorithms]. Vestnik mashinostroeniya i avtomatizatsii, (5).

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