Technique for Defining the Optimal Parameters of Moving Window at Vibration Accelerometer Signal Processing

dc.citation.epage152
dc.citation.issue2
dc.citation.journalTitleЕнергетика та системи керування
dc.citation.spage142
dc.citation.volume10
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationТзОВ “Техприлад”
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.affiliationTechprylad LLC
dc.contributor.authorФедоришин, Роман
dc.contributor.authorЛимич, Василь
dc.contributor.authorЗаграй, Володимир
dc.contributor.authorМасняк, Олег
dc.contributor.authorFedoryshyn, Roman
dc.contributor.authorLymych, Vasyl
dc.contributor.authorZagraj, Volodymyr
dc.contributor.authorMasniak, Oleh
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-10-20T09:16:20Z
dc.date.created2024-02-27
dc.date.issued2024-02-27
dc.description.abstractУ статті наведено методику визначення оптимальних параметрів рухомого вікна під час опрацювання сигналу вібраційного акселерометра, встановленого на кульовому барабанному млині у складі системи автоматизації. На основі експериментальних даних частотних спектрів синтезовано часові сигнали віброприскорення із застосуванням зворотного перетворення Фур’є. Визначено верхню та нижню межі розміру рухомого вікна. Частотний спектр для часового сигналу в рухомому вікні побудовано методом швидкого перетворення Фур’є. Запропоновано критерій оптимальності, який враховує якість побудованого частотного спектра та обчислювальні ресурси мікропроцесорної системи, необхідні для опрацювання сигналу віброакселерометра. Оптимальна тривалість рухомого вікна для аналізованого прикладу становить 100 мс. Досліджено вплив частоти дискретизації часового сигналу на форму побудованого частотного спектра.
dc.description.abstractThis paper presents a technique for defining the optimal parameters of a moving window when processing the signal of a vibration accelerometer installed on a ball drum mill as part of the automation system. Time series signals of the vibration acceleration have been synthesized based on the experimental data of frequency spectrums with the application of the inverse Fourier transform. The lower and upper limits for the moving window size have beendefined. The frequency spectrum for the time series signal within the moving window has been built by means of the fast Fourier transform method. An optimality criterion has been proposed. This criterion considers the quality of the derived frequency spectrum and the computational resources of the microprocessor system needed for processing the vibration accelerometer signal. The optimal duration of the moving window for the analyzed example is 100 ms. The impact of the time signal sampling rate on the frequency spectrum shape has been studied.
dc.format.extent142-152
dc.format.pages11
dc.identifier.citationTechnique for Defining the Optimal Parameters of Moving Window at Vibration Accelerometer Signal Processing / Roman Fedoryshyn, Vasyl Lymych, Volodymyr Zagraj, Oleh Masniak // Energy Engineering and Control Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 10. — No 2. — P. 142–152.
dc.identifier.citationenTechnique for Defining the Optimal Parameters of Moving Window at Vibration Accelerometer Signal Processing / Roman Fedoryshyn, Vasyl Lymych, Volodymyr Zagraj, Oleh Masniak // Energy Engineering and Control Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 10. — No 2. — P. 142–152.
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/113853
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofЕнергетика та системи керування, 2 (10), 2024
dc.relation.ispartofEnergy Engineering and Control Systems, 2 (10), 2024
dc.relation.references[1] S. Mohanty, K. K. Gupta, K. S. Raju (2015) Vibration feature extraction and analysis of industrial ball mill using MEMS accelerometer sensor and synchronized data analysis technique. Procedia Computer Science, Vol. 58, P. 217-224, https://doi.org/10.1016/j.procs.2015.08.058
dc.relation.references[2] Ting Wang, Wenjie Zou, Ruijing Xu, Huaibing Xu, Le Tao, Jianjun Zhao, Yi He. (2021). Assessing load in ball mill using instrumented grinding media. Minerals Engineering, Vol. 173, 107198. https://doi.org/10.1016/j.mineng.2021.107198
dc.relation.references[3] Hassan, I. U.,; Panduru, K.; Walsh, J. (2024). An in-depth study of vibration sensors for condition monitoring. Sensors, 24, 740. https://doi.org/10.3390/s24030740
dc.relation.references[4] Gren Ya. Programming of real-time systems: a textbook. Lviv Polytechnic Publishing House, Lviv, 2011, 324 p.
dc.relation.references[5] Huang, P., Jia, M. & Zhong, B. (2014). Study on the method for collecting vibration signals from mill shell based on measuring the fill level of ball mill. Mathematical Problems in Engineering, Vol. 2014, Article ID 472315, 10 p. https://doi.org/10.1155/2014/472315
dc.relation.references[6] Jeong, H., Yu, J., Lee, Y., Ryu, S. S., & Kim, S. (2022). Real-time slurry characteristic analysis during ball milling using vibration data. Journal of Asian Ceramic Societies, 10(2), 430–437. https://doi.org/10.1080/21870764.2022.2068747
dc.relation.references[7] Tang, W., Zhang, F., Luo, X., Wan, J., and Deng, T. (2023). Method of vibration signal processing and load-type identification of a mill based on ACMD-SVD. Mineral Resources Management, 39(1), pp. 217–233. https://doi.org/10.24425/gsm.2023.144626
dc.relation.references[8] Zhan, D.; Lu, D.; Gao, W.; Wei, H.; Sun, Y. (2024). Chatter detection in thin-wall milling based on multi-sensor fusion and dual-stream residual attention CNN. Machines, 12, 559. https://doi.org/10.3390/machines12080559
dc.relation.references[9] Zhang, X., Wang, S., Li, W. and Lu, X. (2021) Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction. The International Journal of Advanced Manufacturing Technology, 114, 2651–2675. https://doi.org/10.1007/s00170-021-07021-6
dc.relation.references[10] Brigham E. Oran. The Fast Fourier Transform and Its Applications. New York: Prentice-Hall, 1988.
dc.relation.references[11] https://www.mathworks.com/help/matlab/ref/fft.html (accessed on 15.11.2024)
dc.relation.references[12] Pistun, Y[evhen]; Fedoryshyn, R[oman]; Zagraj, V[olodymyr]; Nykolyn, H[ryhoriy] & Kokoshko, R[oman] (2019). Experimental Study and Mathematical Modelling of Nonlinear Control Plant, Proceedings of the 30th DAAAM International Symposium, pp. 0967–0975, B. Katalinic (ed.), Published by DAAAM International, ISBN 978-3-902734-22-8, ISSN 1726-9679, Vienna, Austria. https://doi.org/10.2507/30th.daaam.proceedings.134
dc.relation.references[13] https://www.mathworks.com/help/matlab/ref/ifft.html (accessed on 15.11.2024)
dc.relation.references[14] A. V. Oppenheim, A. S. Willsky, S. H. Nawab. Signals and Systems. 2nd ed. Prentice Hall, 1997.
dc.relation.references[15] R. B. Randall. Frequency Analysis. Bruel & Kjaer, 1987.
dc.relation.referencesen[1] S. Mohanty, K. K. Gupta, K. S. Raju (2015) Vibration feature extraction and analysis of industrial ball mill using MEMS accelerometer sensor and synchronized data analysis technique. Procedia Computer Science, Vol. 58, P. 217-224, https://doi.org/10.1016/j.procs.2015.08.058
dc.relation.referencesen[2] Ting Wang, Wenjie Zou, Ruijing Xu, Huaibing Xu, Le Tao, Jianjun Zhao, Yi He. (2021). Assessing load in ball mill using instrumented grinding media. Minerals Engineering, Vol. 173, 107198. https://doi.org/10.1016/j.mineng.2021.107198
dc.relation.referencesen[3] Hassan, I. U.,; Panduru, K.; Walsh, J. (2024). An in-depth study of vibration sensors for condition monitoring. Sensors, 24, 740. https://doi.org/10.3390/s24030740
dc.relation.referencesen[4] Gren Ya. Programming of real-time systems: a textbook. Lviv Polytechnic Publishing House, Lviv, 2011, 324 p.
dc.relation.referencesen[5] Huang, P., Jia, M. & Zhong, B. (2014). Study on the method for collecting vibration signals from mill shell based on measuring the fill level of ball mill. Mathematical Problems in Engineering, Vol. 2014, Article ID 472315, 10 p. https://doi.org/10.1155/2014/472315
dc.relation.referencesen[6] Jeong, H., Yu, J., Lee, Y., Ryu, S. S., & Kim, S. (2022). Real-time slurry characteristic analysis during ball milling using vibration data. Journal of Asian Ceramic Societies, 10(2), 430–437. https://doi.org/10.1080/21870764.2022.2068747
dc.relation.referencesen[7] Tang, W., Zhang, F., Luo, X., Wan, J., and Deng, T. (2023). Method of vibration signal processing and load-type identification of a mill based on ACMD-SVD. Mineral Resources Management, 39(1), pp. 217–233. https://doi.org/10.24425/gsm.2023.144626
dc.relation.referencesen[8] Zhan, D.; Lu, D.; Gao, W.; Wei, H.; Sun, Y. (2024). Chatter detection in thin-wall milling based on multi-sensor fusion and dual-stream residual attention CNN. Machines, 12, 559. https://doi.org/10.3390/machines12080559
dc.relation.referencesen[9] Zhang, X., Wang, S., Li, W. and Lu, X. (2021) Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction. The International Journal of Advanced Manufacturing Technology, 114, 2651–2675. https://doi.org/10.1007/s00170-021-07021-6
dc.relation.referencesen[10] Brigham E. Oran. The Fast Fourier Transform and Its Applications. New York: Prentice-Hall, 1988.
dc.relation.referencesen[11] https://www.mathworks.com/help/matlab/ref/fft.html (accessed on 15.11.2024)
dc.relation.referencesen[12] Pistun, Y[evhen]; Fedoryshyn, R[oman]; Zagraj, V[olodymyr]; Nykolyn, H[ryhoriy] & Kokoshko, R[oman] (2019). Experimental Study and Mathematical Modelling of Nonlinear Control Plant, Proceedings of the 30th DAAAM International Symposium, pp. 0967–0975, B. Katalinic (ed.), Published by DAAAM International, ISBN 978-3-902734-22-8, ISSN 1726-9679, Vienna, Austria. https://doi.org/10.2507/30th.daaam.proceedings.134
dc.relation.referencesen[13] https://www.mathworks.com/help/matlab/ref/ifft.html (accessed on 15.11.2024)
dc.relation.referencesen[14] A. V. Oppenheim, A. S. Willsky, S. H. Nawab. Signals and Systems. 2nd ed. Prentice Hall, 1997.
dc.relation.referencesen[15] R. B. Randall. Frequency Analysis. Bruel & Kjaer, 1987.
dc.relation.urihttps://doi.org/10.1016/j.procs.2015.08.058
dc.relation.urihttps://doi.org/10.1016/j.mineng.2021.107198
dc.relation.urihttps://doi.org/10.3390/s24030740
dc.relation.urihttps://doi.org/10.1155/2014/472315
dc.relation.urihttps://doi.org/10.1080/21870764.2022.2068747
dc.relation.urihttps://doi.org/10.24425/gsm.2023.144626
dc.relation.urihttps://doi.org/10.3390/machines12080559
dc.relation.urihttps://doi.org/10.1007/s00170-021-07021-6
dc.relation.urihttps://www.mathworks.com/help/matlab/ref/fft.html
dc.relation.urihttps://doi.org/10.2507/30th.daaam.proceedings.134
dc.relation.urihttps://www.mathworks.com/help/matlab/ref/ifft.html
dc.rights.holder© Національний університет “Львівська політехніка”, 2024
dc.subjectвіброакселерометр
dc.subjectперетворення Фур’є
dc.subjectчастотний спектр
dc.subjectчасовий сигнал
dc.subjectкульовий барабанний млин
dc.subjectvibration accelerometer
dc.subjectFourier transform
dc.subjectfrequency spectrum
dc.subjecttime series signal
dc.subjectball drum mill
dc.titleTechnique for Defining the Optimal Parameters of Moving Window at Vibration Accelerometer Signal Processing
dc.title.alternativeМетодика визначення оптимальних параметрів рухомого вікна для опрацювання сигналу віброакселерометра
dc.typeArticle

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