ALGORITHM OF CLASSIFICATION OF SHAFT ORBITS
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Abstract
Classification of spatial patterns of shaft orbits is studied in this paper. Recent methods of signal processing, such as spectral interference frequency refinement method, Mallat scattering transform were tested for task of obtaining patterns, informative features extraction and classification. Strong dependence on fluctuations of signal parameters and significant variability of spatial patterns has been discussed. Effectiveness of ranking of patterns using different approaches has been estimated using computational modelling and natural experiments. Preprocessing of signal and informative features has been considered. Approach of discrimination of different misalignment types and severities, based on rate of occurrence of classes of spatial patterns, has been proposed, its effectiveness has been demonstrated.
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References
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.