Errors classification method for electric motor torque measurement

• Main Authors: Mykola Kulyk, Volodymyr Kvasnikov, Dmytro Kvashuk, Anatolii Beridze-Stakhovskyi
• Format: Article
• Language: English
• Published: PC Technology Center 2021-07-01
• Series: Technology Audit and Production Reserves
• Subjects: error of measuring devices; k-nearest neighbors method; electric motor torque; error estimation tools; data sampling
• Online Access: http://journals.uran.ua/tarp/article/view/237273

The use of high-precision measuring instruments for determining the torque of electric motors in such areas as medicine, motor transport, shipping, aviation requires the improvement of the metrological characteristics of measuring instruments. This, in turn, requires an accurate assessment of their error. Of particular importance is the measurement of power at high-speed installations, where in some cases conventional measurement systems are either unsuitable or have low accuracy. Thus, the use of high-speed turbomachines in aviation, transport, and rocketry creates an urgent need for the development of high-quality measuring instruments for conducting precise research. In turn, in the absence of means for accurately determining the error, attempts are made to predict them. This makes it possible to timely identify the influence of many factors on the accuracy of measuring instruments. The increase in the error arises, as a rule, through abrupt changes in the measurement conditions. Such errors are unpredictable, and their significance is difficult to predict. In the course of the study, the K-nearest neighbors method was used, to establish criteria for which a gross error may occur. The results obtained make it possible to establish threshold values at which the maximum deviation can be established under various conditions of the experiment. In a computational experiment using the K-nearest neighbors method, the following factors were investigated: vibration; temperature rise of measuring sensors; instabilities in the supply voltage of the electric motor, which affect the accuracy of the strain gauge and frequency converter. As a result, the maximum errors were obtained depending on the indicated influence factors. It has been experimentally confirmed that the K-nearest neighbors method can be used to classify deviations of the nominal value of the error of measuring instruments under various measurement conditions. A metrological stand has been developed for the experiment. It includes a strain gauge sensor for measuring torque and a photosensitive sensor for measuring the speed of the electric motor. Signal conversion from these sensors is implemented on the basis of the ESP8266 microcontroller