Digital measurement of shaft rotation speed based on encoder signal: error analysis and adaptive method
DOI:
https://doi.org/10.62911/ete.2025.03.02.09Keywords:
encoder, sensor rotation angle, digital speed measurement, methodical error, dynamic error, microcontroller, process control system.Abstract
The article analyzes the errors of digital measurement of speed (DVS) of rotation of the electric drive shaft using the signal of the angle sensor (AOS), which is part of the encoder, since in modern process automation systems (AUS TP) two different measuring devices are traditionally used to control the position and speed of rotation of the drive motor shaft: an encoder and a tachogenerator. The aim of the work is to develop an adaptive method for digital measurement of shaft rotation speed with reduced methodological error based on the existing encoder. The essence of the method is to count the number of pulses of a highly stable clock generator (timer) during one signal period. The methodological and dynamic components of the error are considered, their analytical dependencies and the results of numerical modeling for low-speed and high-speed mechanisms of metallurgical production are given. A method for reducing methodological error by increasing the measurement base is proposed and tested. The implementation of an economical DCS system based on a 16-bit microcontroller is shown, which provides a combination of position and speed measurement functions in a single sensor and eliminates the need to use a tachogenerator. The cost of the proposed solution is 6-7 times lower than the cost of a traditional scheme using a tachogenerator, since the indicated cost of the proposed solution is about 450 UAH (microcontroller and minimal wiring), which is significantly lower than the cost of an industrial tachogenerator (from 3000 UAH). The exclusion of the tachogenerator also simplifies installation, reduces the dimensions of the control cabinet and increases the reliability of the system by reducing the number of components.
References
Ali, H., Aslam, F., & Ferreira, P. (2021). Modeling Dynamic Multifractal Efficiency of US Electricity Market. Energies, 14(19), 6145. https://doi.org/10.3390/en14196145
Aloui, C., & Mabrouk, S. (2010). Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models. Energy Policy, 38(5), 2326–2339. https://doi.org/10.1016/j.enpol.2009.12.020
Bielinskyi, A. O., Khvostina, I., Mamanazarov, A., Matviychuk, A., Semerikov, S., Serdyuk, O., Solovieva, V., & Soloviev, V. N. (2021b). Predictors of oil shocks. Econophysical approach in environmental science. IOP Conference Series: Earth and Environmental Science, 628(1), 012019. https://doi.org/10.1088/1755-1315/628/1/012019
Fang, W., Gao, X., Huang, S., Jiang, M., & Liu, S. (2018). Reconstructing time series into a complex network to assess the evolution dynamics of the correlations among energy prices. Open Physics, 16(1), 346–354. https://doi.org/10.1515/phys-2018-0047
Hussain, S. I., Nur-Firyal, R., & Ruza, N. (2021). Linkage transitions between oil and the stock markets of countries with the highest COVID-19 cases. Journal of Commodity Markets, 100236. https://doi.org/10.1016/j.jcomm.2021.100236
Joo, K., Suh, J. H., Lee, D., & Ahn, K. (2020). Impact of the global financial crisis on the crude oil market. Energy Strategy Reviews, 30, 100516. https://doi.org/10.1016/j.esr.2020.100516
Kassouri, Y., Bilgili, F., & Kuşkaya, S. (2022). A wavelet-based model of world oil shocks interaction with CO2 emissions in the US. Environmental Science & Policy, 127, 280–292. https://doi.org/10.1016/j.envsci.2021.10.020
Xu, H., Wang, M., & Yang, W. (2020). Information Linkage between Carbon and Energy Markets: Multiplex Recurrence Network Approach. Complexity, 2020, 1–12. https://doi.org/10.1155/2020/5841609
Zebende, G. (2011). DCCA cross-correlation coefficient: Quantifying level of cross-correlation. Physica A: Statistical Mechanics and Its Applications, 390(4), 614–618. https://doi.org/10.1016/j.physa.2010.10.022
Zou, S., & Zhang, T. (2020). Cross-correlation analysis between energy and carbon markets in China based on multifractal theory. International Journal of Low-Carbon Technologies, 15(3), 389–397. https://doi.org/10.1093/ijlct/ctaa010
