[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.
en
pl
