PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Mechanical vibrations analysis in direct drive using CWT with complex morlet wavelet

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Modern industrial process and household equipment more often use direct drives. According to European policy, Industry 4.0 and new Industry 5.0 need to undertake the effort required to ensure a sustainable, human-centric, and resilient European industry. One of the main problems of rotating machines is mechanical vibrations that can limit the lifetime of the final product or the machine in which they are applied. Therefore, analysis of vibration in electrical drives is crucial for appropriate maintenance of the machine. The present article undertakes an analysis of vibration measured at the laboratory stand with multiple dominant frequencies in the range 50-500 Hz. The fast Fourier transform (FFT) gives information about the frequency component without its time localisation. While the solution made available by the short-time Fourier transform (STFT) is able to overcome the problem of FFT, it still has limitations, particularly in terms of there being a lacuna in time and frequency localisation; accordingly, the need is felt for other methods that can give a good localisation in time and frequency. In the article, the continuous wavelet transform (CWT) was investigated, which requires selection of the wavelet function (kernel of transformation). The complex Morlet wavelet was selected with description of its central frequency and bandwidth. CWT and STFT time-frequency localisation capabilities were compared to investigate data registered from the direct-drive laboratory stand. CWT gives better frequency localisation than STFT even for the same frequency resolution. Vibration frequencies with near-locations were separated in CWT and STFT joined them into one wide pick. To ensure a good extraction of frequency features in electric drive systems, the author, based on analysing the results of the present study, recommends that CWT with complex Morlet wavelet be used instead of STFT.
Wydawca
Rocznik
Strony
65--73
Opis fizyczny
Bibliogr.24 poz., rys.
Twórcy
  • Department of Control and Industrial Electronics, Faculty of Automatic Control, Robotics and Electrical Engineering, Poznan University of Technology, 60-965 Poznań, Poland
Bibliografia
  • Brock, S., Łuczak, D., Nowopolski, K., Pajchrowski, T. and Zawirski, K. (2016). Two Approaches to Speed Control for Multi-Mass System with Variable Mechanical Parameters. IEEE Transactions on Industrial Electronics, 99, pp. 1-1. doi: 10.1109/TIE.2016.2598299.
  • Cooley, J. W. and Tukey, J. W. (1965). An Algorithm for the Machine Calculation of Complex Fourier Series. Mathematics of Computation, 19(90), pp. 297-301. doi: 10.2307/2003354.
  • Corinthios, M. J., Smith, K. C. and Yen, J. L. (1975). A Parallel Radix-4 Fast Fourier Transform Computer. IEEE Transactions on Computers, C-24(1), pp. 80-92. doi: 10.1109/T-C.1975.224085.
  • Daubechies, I. (1988). Orthonormal Bases of Compactly Supported Wavelets. Communications on Pure and Applied Mathematics, 41(7), pp. 909-996. doi: 10.1002/cpa.3160410705.
  • European Commission and Directorate-General for Research and Innovation and Breque, M and De Nul, L and Petridis, A (2021). Industry 5.0: Towards A Sustainable, Human Centric and Resilient European Industry. LU: Publications Office of the European Union. Available at: https://data.europa.eu/doi/10.2777/308407 [Accessed: 27 Oct. 2022].
  • European Parliament and Directorate-General for Internal Policies of the Union and Carlberg, M and Kreutzer, S and Smit, J and Moeller, C (2016). Industry 4.0, European Parliament, Policy Department A: Economic and Scientific Policy. Available at: https://www.europarl.europa.eu/RegData/etudes/STUD/2016/570007/IPOL_ STU(2016)570007_EN.pdf
  • Duda, T. Mülder, C., Jacobs, G., Hameyer, K., Bosse, D. and Cardaun, M. (2021). Integration of Electromagnetic Finite Element Models in a Multibody Simulation to Evaluate Vibrations in Direct-Drive Generators. Forschung im Ingenieurwesen, 85(2), pp. 257-264. doi: 10.1007/ s10010-021-00472-z
  • Gao, R. X. and Yan, R. (2011). Continuous Wavelet Transform. In: R. X. Gao and R. Yan, eds., Wavelets: Theory and Applications for Manufacturing. Boston, MA: Springer US, pp. 33-48. doi: 10.1007/978-1- 4419-1545-0_3.
  • Gong, C. and Deng, F. (2022). Design and Optimization of a High-Torque-Density Low-Torque-Ripple Vernier Machine Using Ferrite Magnets for DirectDrive Applications. IEEE Transactions on Industrial Electronics, 69(6), pp. 5421-5431. doi: 10.1109/ TIE.2021.3090714
  • Han, T., Ding, L., Qi, D., Li, C., Fu, Z. and Chen, W. (2022). Compound faults diagnosis method for wind turbine mainshaft bearing with Teager and second-order stochastic resonance. Measurement, 202, p. 111931. doi: 10.1016/j. measurement.2022.111931.
  • Lee, G. R., Gommers, R., Waselewski, F., Wohlfahrt, K. and O’Leary, A. (2019). PyWavelets: A Python Package for Wavelet Analysis. Journal of Open Source Software, 4(36), p. 1237. doi: 10.21105/ joss.01237.
  • Łuczak, D. (2021). Nonlinear Identification with Constraints in Frequency Domain of Electric Direct Drive with Multi-Resonant Mechanical Part. Energies, 14(21), p. 7190. doi: 10.3390/ en14217190.
  • Luczak, D. and Zawirski, K. (2015). Parametric identification of multi-mass mechanical systems in electrical drives using nonlinear least squares method. In: IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, Yokohama, Japan, 9-12 November 2015, pp. 004046-004051. doi: 10.1109/ IECON.2015.7392730.
  • Miletic, F. M., Jovancic, P. D., Milovancevic, M. D., Tanasijevic, M. L. and Djenadic, S. P. (2022). Determining the Impact of Cutting Elements State on the Bucket-Wheel Excavator Vibration and Energy Consumption. Journal of Vibration Engineering and Technologies, 10(5), pp. 1765-1777. doi: 10.1007/s42417-022-00482-3.
  • Nowopolski, K., Wicher, B., Łuczak, D. and Siwek, P. (2017). Recursive neural network as speed controller for two-sided electrical drive with complex mechanical structure. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, 28-31 August 2017, pp. 576-581. doi: 10.1109/ MMAR.2017.8046892.
  • Peeters, C., Guillaume, P. and Helsen, J. (2018). Vibration-Based Bearing Fault Detection for Operations and Maintenance Cost Reduction in Wind Energy. Renewable Energy, 116, pp. 74-87. doi: 10.1016/j.renene.2017.01.056.
  • Pindoriya, R. M., Mishra, A. K., Rajpurohit, B. S. and Kumar, R. (2018). An analysis of vibration and acoustic noise of BLDC motor drive. In: 2018 IEEE Power and Energy Society General Meeting (PESGM). 2018 IEEE Power and Energy Society General Meeting (PESGM), Portland, OR, USA, 5-10 August 2018, pp. 1-5. doi: 10.1109/ PESGM.2018.8585750.
  • Ramteke, S. M., Chelladurai, H. and Amarnath, M. (2022). Diagnosis and Classification of Diesel Engine Components Faults Using Time–Frequency and Machine Learning Approach. Journal of Vibration Engineering and Technologies, 10(1), pp. 175-192. doi: 10.1007/s42417-021-00370-2.
  • Strakosch, F., Nikoleizig, H. and Derbel, F. (2021). Analysis and evaluation of vibration sensors for predictive maintenance of large gears with an appropriate test bench. In: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Glasgow, UK, 17-20 May 2021, pp. 1-6. doi: 10.1109/ I2MTC50364.2021.9460047.
  • Szabat, K., Wróbel, K., Dróżdż, K., Janiszewski, D., Pajchrowski, T. and Wójcik, A. (2020). A Fuzzy Unscented Kalman Filter in the Adaptive Control System of a Drive System with a Flexible Joint. Energies, 13(8), p. 2056. doi: 10.3390/en13082056.
  • Teixeira, J. E. and Tavares-Lehmann, A. T. C. P. (2022). Industry 4.0 in the European Union: Policies and National Strategies. Technological Forecasting and Social Change, 180, p. 121664. doi: 10.1016/j. techfore.2022.121664.
  • Teolis, A. (1998). Computational Signal Processing with Wavelets. Springer, Birkhäuser Boston, MA, USA.
  • Urbanski, K. and Janiszewski, D. (2021). Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks. Energies, 14(23), p. 8134. doi: 10.3390/en14238134.
  • Wszołek, G., Czop, P., Słoniewski, J. and Dogrusoz, H. (2020). Vibration Monitoring of CNC Machinery Using MEMS Sensors. Journal of Vibroengineering, 22(3), pp. 735-750. doi: 10.21595/jve.2019.20788.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-aefed75f-b154-4e91-ac93-0958493cb41c
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.