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Detection and diagnosis of bearing defects using vibration signal processing

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This work presents an analysis of vibration signals for bearing defects using a proposed approach that includes several methods of signal processing. The goal of the approach is to efficiently divide the signal into two distinct components: a meticulously organized segment that contains relatively straightforward information, and an inherently disorganized segment that contains a wealth of intricately complex data. The separation of the two component is achieved by utilizing the weighted entropy index (WEI) and the SVMD algorithm. Information about the defects was extracted from the envelope spectrum of the ordered and disordered parts of the vibration signal. Upon applying the proposed approach to the bearing fault signals available in the Paderborn university database, a high amplitude peak can be observed in the outer ring fault frequency (45.9 Hz). Likewise, for the signals available in XJTU-SY, a peak is observed at the fault frequency (108.6 Hz).
Rocznik
Tom
Strony
433--452
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
  • Electromechanical Engineering Laboratory, Badji Mokhtar University, Annaba, Algeria
  • Electromechanical Engineering Laboratory, Badji Mokhtar University, Annaba, Algeria
  • Electromechanical Engineering Laboratory, Badji Mokhtar University, Annaba, Algeria
Bibliografia
  • [1] A. Nabhan, N.M. Ghazaly, A. Samy, and M.O. Mousa. Bearing fault detection techniques – a review. Turkish Journal of Engineering, Sciences and Technology, 3(2):1–18, 2015.
  • [2] P.P. Kharche and S.V. Kshirsagar. Review of fault detection in rolling element bearing. International Journal of Innovative Research in Advanced Engineering, 1(5):169–174, 2014.
  • [3] Y. Du, S. Zhou, X. Jing, Y. Peng, H. Wu, and N. Kwok. Damage detection techniques for wind turbine blades: A review. Mechanical Systems and Signal Processing, 141:106445, 2020. doi: 10.1016/j.ymssp.2019.106445.
  • [4] Z. Hameed, Y.S. Hong, Y.M. Cho, S.H. Ahn, and C.K. Song. Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews, 13(1):1–39, 2009. doi: 10.1016/j.rser.2007.05.008.
  • [5] K. Bouaouiche, Y. Menasria, and D. Khalfa. Diagnosis of rotating machine defects by vibration analysis. Acta IMEKO, 12(1):1–6, 2023. doi: 10.21014/actaimeko.v12i1.1438.
  • [6] S. Riaz, H. Elahi, K. Javaid, and T. Shahzad. Vibration feature extraction and analysis for fault diagnosis of rotating machinery-a literature survey. Asia Pacific Journal of Multidisciplinary Research, 5(1):103–110, 2017.
  • [7] M. Avoci Ugwiri, M. Mpia, and A. Lay-Ekuakille. Vibrations for fault detection in electric machines. IEEE Instrumentation & Measurement Magazine, 23(1):66–72, 2020. doi: 10.1109/MIM.2020.8979527.
  • [8] T. Liu, S. Yan, and W. Zhang. Time–frequency analysis of nonstationary vibration signals for deployable structures by using the constant-Q nonstationary gabor transform. Mechanical Systems and Signal Processing, 75:228–244, 2016. doi: 10.1016/j.ymssp.2015.12.015.
  • [9] K. Dragomiretskiy and D. Zosso. Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3):531–544, 2014. doi: 10.1109/TSP.2013.2288675.
  • [10] M. Nazari and S.M. Sakhaei. Successive variational mode decomposition. Signal Processing, 174:107610, 2020. doi: /10.1016/j.sigpro.2020.107610.
  • [11] Y. Miao, B. Zhang, J. Lin, M. Zhao, H. Liu, Z. Liu, and H. Li. A review on the application of blind deconvolution in machinery fault diagnosis. Mechanical Systems and Signal Processing, 163:108202, 2022. doi: 10.1016/j.ymssp.2021.108202.
  • [12] T. Barszcz and N. Sawalhi. Fault detection enhancement in rolling element bearings using the minimum entropy deconvolution. Archives of Acoustics, 37(2):131–141, 2012. doi: 10.2478/v10168-012-0019-2.
  • [13] T. Barszcz and A. Jabłoński. A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mechanical Systems and Signal Processing, 25(1):431–451, 2011. doi: 10.1016/j.ymssp.2010.05.018.
  • [14] A. Moshrefzadeh and A. Fasana. The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis. Mechanical Systems and Signal Processing, 105:294–318, 2018. doi: 10.1016/j.ymssp.2017.12.009.
  • [15] D. Neupane and J. Seok. Bearing fault detection and diagnosis using Case Western Reserve University dataset with deep learning approaches: A review. IEEE Access, 8:93155–93178, 2020. doi: 10.1109/ACCESS.2020.2990528.
  • [16] K. Bouaouiche, Y. Menasria, and D. Khalifa. Detection of defects in a bearing by analysis of vibration signals. Diagnostyka, 24(2):2023203, 2023. doi: 10.29354/diag/162230.
  • [17] G. Chen, W. Xie, and Y. Zhao. Wavelet-based denoising: A brief review. In 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP), pages 570–574, Beijing, China, 2013. IEEE. doi: 10.1109/ICICIP.2013.6568140.
  • [18] M. Rhif, B.A. Abbes, I.R. Farah, B. Martínez, and Y.-F. Sang. Wavelet transform application for/in non-stationary time-series analysis: A review. Applied Sciences, 9(7):1345, 2019. doi: 10.3390/app9071345.
  • [19] A. Dibaj, R. Hassannejad, M.M. Ettefagh, and M.M. Ehghaghi. Incipient fault diagnosis of bearings based on parameter-optimized VMD and envelope spectrum weighted kurtosis index with a new sensitivity assessment threshold. ISA Transactions, 114:413–433, 2021. doi: 10.1016/j.isatra.2020.12.041.
  • [20] Y. Li, W. Sun, R. Jiang, and Y. Han. Signal-segments cross-coherence method for nonlinear structural damage detection using free-vibration signals. Advances in Structural Engineering, 23(6):1041–1054, 2020. doi: 10.1177/1369433219886962.
  • [21] J. Yang, C. Zhou, and X. Li. Research on fault feature extraction method based on parameter optimized variational mode decomposition and robust independent component analysis. Coatings, 12(3):419, 2022. doi: 10.3390/coatings12030419.
  • [22] R.M. Mehmood, R. Du, and H.J. Lee. Optimal feature selection and deep learning ensembles method for emotion recognition from human brain EEG sensors. IEEE Access, 5:14797–14806, 2017. doi: 10.1109/ACCESS.2017.2724555.
  • [23] S.-H. Oh, Y.-R. Lee, and H.-N. Kim. A novel EEG feature extraction method using Hjorth parameter. International Journal of Electronics and Electrical Engineering, 2(2):106–110, 2014. doi: 10.12720/ijeee.2.2.106-110.
  • [24] Z. Wang, J. Zhou, J. Wang, W. Du, J. Wang, X. Han, and G. He. A novel fault diagnosis method of gearbox based on maximum kurtosis spectral entropy deconvolution. IEEE Access, 7:29520–29532, 2019. doi: 10.1109/ACCESS.2019.2900503.
  • [25] B. Bono, J. Arnau, R. Alarcón, and M.J. Blanca. Bias, precision, and accuracy of skewness and kurtosis estimators for frequently used continuous distributions. Symmetry, 12(1):19, 2019. doi: 10.3390/sym12010019.
  • [26] S. Kim, D. An, and J.-H. Choi. Diagnostics 101: A tutorial for fault diagnostics of rolling element bearing using envelope analysis in MATLAB. Applied Sciences, 10(20):7302, 2020. doi: 10.3390/app10207302.
  • [27] X. Ye, Y. Hu, J. Shen, R. Feng, and G. Zhai. An improved empirical mode decomposition based on adaptive weighted rational quartic spline for rolling bearing fault diagnosis. IEEE Access, 8:123813–123827, 2020. doi: 10.1109/ACCESS.2020.3006030.
  • [28] V. Kannan, H. Li, and D.V. Dao. Demodulation band optimization in envelope analysis for fault diagnosis of rolling element bearings using a real-coded genetic algorithm. IEEE Access, 7:168828–168838, 2019. doi: 10.1109/ACCESS.2019.2954704.
  • [29] P.H. Jain and S.P. Bhosle. Analysis of vibration signals caused by ball bearing defects using timedomain statistical indicators. International Journal of Advanced Technology and Engineering Exploration, 9(90):700, 2022. doi: 10.19101/IJATEE.2021.875416.
  • [30] C.R. Soto-Ocampo, J.M. Mera, J.D. Cano-Moreno, and J.L. Garcia-Bernardo. Low-cost, highfrequency, data acquisition system for condition monitoring of rotating machinery through vibration analysis-case study. Sensors, 20(12):3493, 2020. doi: 10.3390/s20123493.
  • [31] XJTU-SY bearing database. https://biaowang.tech/xjtu-sy-bearing-datasets/.
  • [32] Paderborn University database. http://mb.uni-paderborn.de/kat/datacenter.
  • [33] G.L. McDonald, Q. Zhao, and M.J. Zuo. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection. Mechanical Systems and Signal Processing, 33:237–255, 2012. doi: 10.1016/j.ymssp.2012.06.010.
  • [34] H. Cui, Y. Guan, and H. Chen. Rolling element fault diagnosis based on VMD and sensitivity MCKD. IEEE Access, 9:120297–120308, 2021. doi: 10.1109/ACCESS.2021.3108972.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b19109e9-27d6-45a6-af0d-f1dbf4798466
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