АЛГОРИТМ КЛАССИФИКАЦИИ ОРБИТ ВАЛА

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Д. А. КЕЧИК
И. Г. ДАВЫДОВ
И. В. ЛОЩИНИН
К. Д. ЖУКОВСКИЙ

Аннотация

В настоящей работе рассматривается классификация пространственных шаблонов орбиты вала. Опробовано применение современных методов обработки сигналов (метод спектральной интерференции и рассеивающее преобразование Малла) в задаче получения пространственных шаблонов, извлечения информативных признаков и классификации. Рассматривалась сильная зависимость пространственных шаблонов от флуктуаций параметров сигнала и непостоянство их формы. Оценивалась эффективность классификации пространственных шаблонов при использовании различных подходов в ходе численного эксперимента и натурного моделирования. Рассмотрена предобработка сигнала и извлеченных информативных признаков. Предложен подход к различению типа и степени выраженности расцентровки валов, основанный на частоте встречаемости различных классов пространственных шаблонов, показана эффективность подхода.

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Как цитировать
КЕЧИК, Д. А. ., ДАВЫДОВ, И. Г., ЛОЩИНИН, И. В., & ЖУКОВСКИЙ, К. Д. (2021). АЛГОРИТМ КЛАССИФИКАЦИИ ОРБИТ ВАЛА. Вестник Полоцкого государственного университета. Серия С. Фундаментальные науки, (4), 35-44. извлечено от https://journals.psu.by/fundamental/article/view/855
Выпуск
Раздел
Информационные технологиии
Биография автора

И. Г. ДАВЫДОВ, Белорусский государственный университет информатики и радиоэлектроники, Минск

PhD

Библиографические ссылки

Ghafari, S.H. A Fault Diagnosis System for Rotary Machinery Supported by Rolling Element Bearings / S.H. Ghafari.

Jordan, M.A. Paper about orbit plots / M.A. Jordan // Orbit. – 1993.

Shirman, A.R. Practice of vibrational diagnosing and monitoring of equipment state/ A.R. Shirman, A.B. Solovyov. –Moskow : Nauka, 1996. – 276 p. (in Russian)

Vibrational diagnosing / Ye.Z. Madorskiy [et al.] ; ed. G.Sh. Rosenberg. – St. Petrsbourg : Federal State educational establishment, 2003. – 284 p. (in Russian)

Rusov V.A. Spectral vibrational diagnosing / V.A. Rusov. – Perm : Vibro-center LLC, 1996. – 215 p. (in Russian)

Rusov V.A. Diagnosing of defects of rotary equipment relying on vibrational signals / V.A. Rusov. – Perm, 2012. (in Russian)

Aherwar, A. Vibration analysis techniques for gearbox diagnostic: A review / A. Aherwar, S. Khalid // Int J Adv Eng Technol. – 2012. – Vol. 3. – P. 4–12.

Sait, A.S. A Review of Gearbox Condition Monitoring Based on vibration Analysis Techniques Diagnostics and Prognostics / A.S. Sait, Y.I. Sharaf-Eldeen // Rotating Machinery, Structural Health Monitoring, Shock and Vibration, Volume 5 : Conference Proceedings of the Society for Experimental Mechanics Series / Springer ; ed. T. Proulx. – New York, NY, 2011. – P. 307–324.

Bechhoefer, E. A Review of Time Synchronous Average Algorithms / E. Bechhoefer, M. Kingsley. – 2009. – P. 10.

Zhang, X. A new time synchronous average method for variable speed operating condition gearbox / X. Zhang, G. Wen, T. Wu // J. Vibroengineering. – 2012. – Vol. 14, № 4. – P. 1766–1774.

Kechik, D. Segmented Autoregression Pitch Estimation Method / D. Kechik, I. Davydov // 2020 International Conference on Dynamics and Vibroacoustics of Machines (DVM) : 2020 International Conference on Dynamics and Vibroacoustics of Machines (DVM). – 2020. – P. 1–6.

Al-Khazali, H.A.H. Geometrical and Graphical Representations Analysis of Lissajous Figures in Rotor Dynamic System / H.A.H. Al-Khazali // IOSR J. Eng. – 2012. – Vol. 02, № 05. – P. 971–978.

Gavrilov A.M. Studying of Lissajous figures for measuring of phase relations in spectrum of triharmonic signal / A.M. Gavrilov, R.O. Sitnikov // University news. North-caucasian region. Technical sciences series. – 2006. – No. 3. – P. 34–39. (in Russian)

Genkin, M.D. Vibroacoustic diagnostics of machines and mechanisms / M.D. Genkin, A.G. Sokolova. – Moscow : Mashinostroenie, 1987. – 288 p. (In Russian)

Kosmach, N.V. Approach of vibrational diagnosing of rolling bearing / N.V. Kosmach, Yu.P. Aslamov. – 2020.

Rotating Machinery Diagnostics Using Deep Learning on Orbit Plot Images / H. Jeong [et al.] // Procedia Manuf. : 44th North American Manufacturing Research Conference, NAMRC 44, June 27 – July 1, 2016, Blacksburg, Virginia, United States. – 2016. – Vol. 5. – P. 1107–1118.

Polar and Orbit Plot Analysis for Unbalance Identification in A Rotating System / A. Sen [et al.] // IOSR J. Mech. Civ. Eng. – 2017. – Vol. 14, № 03. – P. 49–56.

Orbit Analysis For Imbalance Fault Detection In Rotating Machinery / C. Costa [et al.] // IOSR J. Electr. Electron. Eng. IOSR-JEEE. – 2018. – Vol. 13. – P. 43–53.

Goldin, A.S. Vibration of rotary machines / A.S. Goldin. – Moskow : Mechanical engineering, 1999. – 344 p. (in Russian)

Buscarello, R.T. Practical Solutions to Machinery and Maintenance Vibration Problems / R.T. Buscarello. – Pub. Fifth edition. – Update international Inc., 2011. – 262 p.

Kechik, D.A. Vibrational signal power variance compensation during equipment speed mode changing / D.A. Kechik // Doklady BGUIR. – 2020. – Vol. 18, No 5. – P. 27–34.

Bently, D.E. Fundamentals of Rotating Machinery Diagnostics / D.E. Bently, C.T. Hatch. – ASME, Bently Pressurized Bearing Company, 2002. – 726 p.

Scheffer, C. Practical Machinery Vibration Analysis and Predictive Maintenance / C. Scheffer, P. Girdhar. – Elsevier, 2004. – 263 p.

Kechik, D. Shaft Misalignment Data for: Inter-component Phase Processing of Quasipolyharmonic Signals / D. Kechik, Y. Aslamov, I. Davydov. – 2020. – DOI: 10.17632/pt9mjcvghd.1.

Kechik, D. Shaft Angular Misalignment Dataset / D. Kechik, Y. Aslamov, I. Davydov. – 2021. – DOI: 10.17632/kf96jx9dzf.1.

Barkov, A.V. Monitoring and diagnostics of rotary equipment relying on its vibration / A.V. Barkov, N.A. Barkova, A.Yu. Azovtsev. – St. Petrsbourg : SMTU, 2000. – 159 p. (in Russian)

Barkov, A.V. Vibrational diagnosing of machines and equipment. Analysis of vibration. Tutorial / Barkov, N.A. Barkova. – St. Petrsbourg : SMTU, 2004. – 156 p. (in Russian)

Influence of changes in shaft rotational speed of rotary equipment on frequency-domain processing / Yu.P. Aslamov [et al.] // Doklady BGUIR. – 2018. – Vol. 113, No 13. – P. 13–18.

Voskobojnikov, Y.E. Filteration of signals and images: Fourier and wavelet algorithms (with examples in Mathcad) / Y.E. Voskobojnikov, A.V. Gochakov, A.B. Kolker. – Novosibirsk : NGASU (Sibstrin), 2010.– 188 p. (In Russian)

Kechik, D. Shaft orbits dataset: parallel and angular misalignment / D. Kechik, I. Davydov. – 2021. – DOI: 10.17632/8b33tx79wt.1.

Vorobiov, V.I. Inter-component phase processing of quasipolyharmonic signals / V.I. Vorobiov, D.A. Kechik, S.Y. Barysenka // Appl. Acoust. – 2021. – Vol. 177. – 14 p.

Barysenka, S.Y. Single-channel speech enhancement using inter-component phase relations / S.Y. Barysenka, V.I. Vorobiov, P. Mowlaee // Speech Commun. – 2018. – Vol. 99. – P. 144–160.

Mallat, S. Group Invariant Scattering / S. Mallat // Commun. Pure Appl. Math. – 2012. – Vol. 65, № 10. – P. 1331–1398.

Bruna, J. Invariant Scattering Convolution Networks / J. Bruna, S. Mallat // IEEE Trans. Pattern Anal. Mach. Intell. – 2013. – Vol. 35, № 8. – P. 1872-1886.

Oyallon, E. Deep roto-translation scattering for object classification / E. Oyallon, S. Mallat // IEEE. – 2015. – P. 2865–2873.

ScatNet: a MATLAB Toolbox for Scattering Networks [Electronic resource]. – Mode of access: https://github.com/scatnet/scatnet/blob/master/doc/impl/impl.pdf. – Date of access: 11.10.2020.

Chang, C.-C. LIBSVM: A library for support vector machines / C.-C. Chang, C.-J. Lin // ACM Trans. Intell. Syst. Technol. – 2011. – Vol. 2, № 3. – 27 p.

Hsu, C.-W. A practical guide to support vector classification / C.-W. Hsu, C.-C. Chang, C.-J. Lin // Journal of Data Analysis and Information Processing. – 2003. – Vol. 8, № 2. – P. 16.

Tharwat, A. Classification assessment methods / A. Tharwat // Applied Computing and Informatics. – 2020. – Vol. 17, № 1. – P. 168–192.