Improvement of the Data Processing Algorithm for Diagnostic System of the State of Continuous Casting Machine Mold
Article Sidebar
Main Article Content
Abstract
The paper is devoted to solving the task of creating the prototype of effective diagnostic system of the state of continuous casting machine mold used in iron and steel works. The relevance of the task is explained by the high cost of known similar systems of foreign production and of their maintenance as well. In addition, there are some technical complications of those systems integration into the equipment of local metallurgy enterprises. The authors have experience of designing the diagnostic systems of such kind, which were tested in several Ukrainian iron and steel works and showed satisfactory results. This work is aimed to improving the technical characteristics of the prototype of diagnostic system of the state of continuous casting machine mold designed by the authors, in order to increase the accuracy of diagnosing malfunctions of the oscillating mechanism of the mold. The state of the mold oscillating mechanism is evaluated by measuring the deviation of its movement trajectory from the reference signal. In the paper, the structure and operation principle of the diagnostic system prototype are shown and substantiated. The mathematical basics for processing data from the sensors used in the system, MEMS accelerometers, are represented. The procedures for improving the accuracy of measurements the mold movement parameters are described. The noise in the output signals of the sensors is defined as an undesirable factor to eliminate because of its effect on the accuracy of measurements. The Kalman filtering is suggested for decreasing the noise in the output signals of MEMS accelerometers. The filtering implementation is described mathematically.
The improved algorithm including Kalman filtering for processing data from MEMS accelerometers, that reduces the measurement errors of the parameters of the mold movement under conditions of noise in the output signals of the sensors is represented. The simulation of the suggested algorithm is performed; the diagram of the sensor output noisy signal of acceleration and the diagram of filtered and integrated signal of displacement illustrating the processes of measuring the parameters of the mold movement and processing the measured data are obtained. The results of the simulation confirmed the effectiveness of the proposed solution. The application of Kalman filtering makes it possible to improve the signal-to-noise ratio from 13.9 dB to 38.7 dB, what is about 25 dB increase, that respectively gives the opportunities to increase the accuracy of mold movement trajectory measurements. In the future works, it is planned to develop this approach by combining it with machine learning, which is able to make the system as effective as possible.
Ref. 15, fig. 4.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
L. Liu, Y. Dun, Y. Fang, and G. Li, “Modeling and verification of the nonlinear system of oscillation platform of continuous casting mold driven by servo motor,” Adv. Mech. Eng., vol. 8, no. 7, pp. 1–9, Jun. 2016. DOI: 10.1177/1687814016656294
A. N. Smirnov, O. V. Antykuz, A. Y. Tsuprun, and V. M. Pil’gaev, “Some approaches to choosing rational mold oscillation parameters,” Russ. Metall., vol. 2008, no. 8, pp. 700–705, Dec. 2008. DOI: 10.1134/S0036029508080119
A. N. Smirnov, S. V. Kuberskii, A. L. Podkorytov, V. E. Ukhin, A. V. Kravchenko, A. Yu. Orobtsev, Nepreryvnaya razlivka sortovoy zagotovki [Continuous casting of billet]. Alchevsk: DonGTU, 2012.
J. Cibulka, R. Krzok, R. Hermann, D. Bocek, J. Cupek, and K. Michalek, “Impact of Oscillation Parameters on Surface Quality of Cast Billets,” Arch. Metall. Mater., vol. 61, no. 1, pp. 283–288, Mar. 2016. URL: http://www.imim.pl/files/archiwum/Vol1_2016/53.pdf
M. Vynnycky, S. Saleem, K. M. Devine, B. J. Florio, S. L. Mitchell, and S. B. G. O’Brien, “On the formation of fold-type oscillation marks in the continuous casting of steel,” R. Soc. Open Sci., vol. 4, no. 6, p. 170062, Jun. 2017. DOI: 10.1098/rsos.170062
“Mould Oscillation Measuring System KS473/KHM,” 2004. [Online]. Available: https://www.mmf.de/pdf/9-1.pdf. [Accessed: 01-Dec-2018].
“OsciMon Exact mold oscillation for perfect casting products,” 2015. [Online]. Available: https://www.primetals.com/fileadmin/user_upload/content/01_portfolio/15_Lifecycle-services/15_2_Diagnostic-systems/OsciMon.pdf. [Accessed: 01-Dec-2018].
“KT400 FieldMOMS Portable Mold Oscillation Monitoring System,” 2019. [Online]. Available: https://www.kisstechnologies.com/wp-content/uploads/2019/03/KT400-ProductSheet-Final.pdf. [Accessed: 01-Apr-2019].
V. A. Didenko, A. F. Bondarenko, A. N. Poleno, “ Obzor sredstv kontrolya trayektorii dvizheniya kristallizatora MNLZ [An overview of the means of controlling the trajectory of the motion of the crystallizer MNLZ],” in Proceedings of the XV International Scientific and Practical Conference "Modern Information and Electronic Technologies (CITI)",” 2014, pp. 58–59.
O. F. Bondarenko and V. O. Didenko, “The system of estimation of the state variables for PWM converter control in the structure of mold oscillation mechanism,” Electron. Commun., vol. 21, no. 5, pp. 14–19, Nov. 2016. DOI: 10.20535/2312-1807.2016.21.5.81934
O. M. Polieno, O. F. Bondarenko, V. O Didenko, “Matematychna modelʹ obrobky danykh v systemi monitorynhu parametriv khytannya krystalizatora mashyny bezperervnoho lyttya zahotovok [Mathematical model of data processing in the system of monitoring the parameters of the rolling machine crystallization machine of continuous casting of billets],” Bulletin of the National Technical University "KhPI" collection of scientific works, thematic issue "New solutions in modern technologies", no. 18(991), pp. 70–76, 2013. URI: http://repository.kpi.kharkov.ua/handle/KhPI-Press/7963
B. Panomruttanarug and R. W. Longman, “The Advantages and Disadvantages of Kalman Filtering in Iterative Learning Control,” Adv. Astronaut. Sci., vol. 130, pp. 347–365, 2008. URL: https://www.researchgate.net/publication/289723787_The_advantages_and_disadvantages_of_kalman_filtering_in_iterative_learning_control
R. Kalman, P, Falb, M. Arbib, Ocherki po matematicheskoy teorii sistem [Essays on the mathematical theory of systems], 2nd ed. Moscow: Editoial URSS, 2004.
D. Simon, “Kalman filtering,” Embed. Syst. Program., vol. 14, no. 6, pp. 72–79, 2001.
N. T. Kuzovkov Modal'noye upravleniye i nablyudayushchiye ustroystva [Modal control and monitoring devices]. Moscow: Mashynostroenie, 1976.
