Implementation of Dynamic Error Correction Algorithm for Self-adjusting Measuring System Based on South Ural State University Supercomputer Center

Dynamic measurements, that is, measurements using measuring instruments in a dynamic mode, have become widespread in scientific research, testing of samples of new equipment and production. The dynamic mode of operation of the sensors is characterized by the appearance of a dynamic error, which turn...

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Bibliographic Details
Published in2019 International Russian Automation Conference (RusAutoCon) pp. 1 - 5
Main Authors Yurasova, E.V., Volosnikov, A.S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
Adaptive Measuring Systems
Distortion measurement
Dynamic Measurement Error
Dynamically Distorted Signal Recovery
Gradient method
Heuristic algorithms
Mathematical model
Measurement uncertainty
Modal Control
Model of a Primary Measuring Transducer
Noise measurement
Sensors
Supercomputer
Supercomputers
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DOI10.1109/RUSAUTOCON.2019.8867804

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Summary:Dynamic measurements, that is, measurements using measuring instruments in a dynamic mode, have become widespread in scientific research, testing of samples of new equipment and production. The dynamic mode of operation of the sensors is characterized by the appearance of a dynamic error, which turns out to be more than all other components of the total measurement error. The known methods for recovering dynamically distorted signals do not take into account the characteristics of random additive noise in the measuring system and require a large amount of a priori information. The adaptive measuring system was synthesized on the basis of the dynamic error correction method under the conditions of real system noise by the criterion of minimizing the total dynamic error. The model of the measuring system takes into account the inertia of the sensor, as well as the noise and interference present at its output. The article presents a mathematical model of a dynamic, self-adjusting measuring system synthesized on the basis of a first-order sensor. The results of supercomputer modeling of the measuring system of the first order are presented. It is concluded that deep mathematical processing of measurement results, performed after the completion of the experiment, reduces the estimate of the dynamic measurement error by 84%.
DOI:10.1109/RUSAUTOCON.2019.8867804