The paper considers the development of measuring electronics, which can be used
in a variety of systems and in a wide range, due to its flexible design and intellectual properties. A
methodology is proposed for creating intelligent sensors that take into account the resources of
multisensor systems, as well as using multi-purpose evolutionary computing. The proposed
methodology solves the problem of rapid prototyping of measurement systems in conditions of
limited implementations. The features of self-correction for a particular type of sensor are
considered, on which the stability of the operation of the entire control system in a dynamic
environment depends. In the work, principle work and intelligent device for connecting parametric
sensors, their structural schematic, and functionality. And also it is suggested digital signal
processing in real time to improve the reliability of measurement results and compensate for
influencing factors. The developed device is equipped with flexible functions that can be expanded
by including intellectual properties. The developed intelligent device for connecting parametric
sensors addresses the issues of a very important problem of diagnosing a measurement sensor.
The proposed device can independently determine the state of the primary measuring transducer,
check and detect malfunctions of measuring instruments. Developed smart device _ has the ability
to adapt to conditions exploitation, also has the ability to connect most parametric sensors.
Developed device allows conduct continuous diagnostics, tracking faults and do credibility
conclusions _ measurements. Composition _ diagnostics included control stability object and state
sensor, and tracking too much weak signal, warning about danger complete failure sensor.
The paper considers the development of measuring electronics, which can be used
in a variety of systems and in a wide range, due to its flexible design and intellectual properties. A
methodology is proposed for creating intelligent sensors that take into account the resources of
multisensor systems, as well as using multi-purpose evolutionary computing. The proposed
methodology solves the problem of rapid prototyping of measurement systems in conditions of
limited implementations. The features of self-correction for a particular type of sensor are
considered, on which the stability of the operation of the entire control system in a dynamic
environment depends. In the work, principle work and intelligent device for connecting parametric
sensors, their structural schematic, and functionality. And also it is suggested digital signal
processing in real time to improve the reliability of measurement results and compensate for
influencing factors. The developed device is equipped with flexible functions that can be expanded
by including intellectual properties. The developed intelligent device for connecting parametric
sensors addresses the issues of a very important problem of diagnosing a measurement sensor.
The proposed device can independently determine the state of the primary measuring transducer,
check and detect malfunctions of measuring instruments. Developed smart device _ has the ability
to adapt to conditions exploitation, also has the ability to connect most parametric sensors.
Developed device allows conduct continuous diagnostics, tracking faults and do credibility
conclusions _ measurements. Composition _ diagnostics included control stability object and state
sensor, and tracking too much weak signal, warning about danger complete failure sensor.
| № | Muallifning F.I.Sh. | Lavozimi | Tashkilot nomi |
|---|---|---|---|
| 1 | Yusupbekov N.R. | teacher | TSTU |
| 2 | Ruziev U.A. | researcher | TSTU |
| № | Havola nomi |
|---|---|
| 1 | U.A. Ruziev. Development of the industrial intelligent measurement converter, International scientific and technical journal. “Chemical technology. Control management”, Tashkent, 2022. 38. |
| 2 | U.A. Ruziev. Development of intelligent sensors with metrological self-control. “Technical sciences and innovation”, Tashkent, 2022. 142. |
| 3 | N.R. Yusupbekov., U.A. Ruziev., M.K Shodiev. Multi-model intellectual virtual Analyzers of Parameters of Technological Processes. Journal "Advances in Intelligent Systems and Computing. “Springer Nature”, Switzerland, 2021. 121. |
| 4 | S. Zhiyuan., W. Miao. “Self-test and self-calibration of digital closed-loop accelerometers, sensors”. Published online 2022 Dec doi : 10.3390/s22249933, Basel, 2022. |
| 5 | Y. Wang., A. Yang., Z. Li., X. Chen., P. Wang., H. Yang. Blind drift calibration of sensor networks using sparse bayesian learning. “IEEE Sensors Journal”, 2016. 6249 |
| 6 | L. Spinelle., M. Gerboles., M.G. Villani., M. Aleixandre., F. Bonavitacola. Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. “Part b: NO, CO and CO2, sensors and actuators B: Chemical”, 2017. 706. |
| 7 | A.S. Semenov., A.L. Shestakov. Model of a self-diagnosing parameter sensor with a nonlinear conversion function. “Metrology and Metrological software: collection of reports of the XXIII national scientific symposium with international participation”, Bulgaria, 2013. 165. |
| 8 | H. Yu., L. Ye., Y. Guo., S. Su. An innovative 9-parameter magnetic calibration method using local magnetic inclination and calibrated acceleration value. “IEEE Sensor”, 2020. 11275. |
| 9 | M.M. Fejer., G.A. Magel., D.H. Jundt., R.L. Byer. “IEEE journal quantum. Electron”, 1992. 2631. |
| 10 | A. Richardson. Use of self-calibration data for multifunctional MEMS sensor prognostics. “IEEE journal of micromechanical systems”, 2016. 761. |
| 11 | S. Braun., R. Freier., A. König. Entwurf und Implementierung eines reconfiguration baren Präzisions-Instrumentierungsverstärkers. “Studienarbeit lehrstuhl integrated sensorsysteme, Betreuer”, Kaiserslautern, 2012. |
| 12 | R. Freier., S. Braun., A. König. Reconfigurable precision instrumentation amplifier for universal sensor interface. “Proceedings of sensors and measuring systems”, Symposium, 2014. 5. |
| 13 | B. Jiang., L. Wang., Y.C. Soh. An adaptive technique for robust diagnosis of faults with independent effects on system outputs. “International journal of control”, 2002. 792. |
| 14 | V. Denisenko. Hart protocol: general information and principles of building networks based on it. “Modern automation technologies”, 2010. 94. |