Bearing fault detection using a method involving absolute value spectrum and impulsivity evaluation
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Electromechanical Engineering Laboratory, Badji Mokhtar-Annaba University,
Submission date: 2023-12-02
Final revision date: 2024-03-31
Acceptance date: 2024-05-07
Online publication date: 2024-05-27
Publication date: 2024-05-27
Corresponding author
Karim Bouaouiche   

Electromechanical Engineering Laboratory, Badji Mokhtar-Annaba University,
Diagnostyka 2024;25(2):2024213
This study analyzes vibration signals related to bearing defects using a method that reconstructs an effective signal. This reconstruction is based on the determination of the instantaneous amplitude and phase. Then, a decomposition method is applied to the amplitude and phase to obtain several simple functions. Once the functions are obtained, an evaluation of impulsivity is performed on each function using the proposed parameter. This selects functions that contain fault data. The important signal is then identified and used. After the creation of the effective signal, filtering by a morphological operator with a structuring element is applied to improve the signal quality. Finally, in the spectrum of the absolute values of this signal, the defect can be detected from the frequency of the peaks. Signals from different databases were analyzed using the proposed method, illustrating the results in the form of high-amplitude peaks in the frequency of bearing component defects.
Source of funding: This research received no external funding.
Gundewar SK, Prasad VK. Condition monitoring and fault diagnosis of induction motor. Journal of Vibration Engineering & Technologies 2021; 9: 643-674.
Du Y. Damage detection techniques for wind turbine blades: A review. Mechanical Systems and Signal Processing 2020; 141: 106445.
Ghazali M, Hazwan M, Rahiman W. Vibration analysis for machine monitoring and diagnosis: a systematic review. Shock and Vibration 2021; 1-25.
Bouaouiche K, Menasria Y, Khalfa D. Diagnosis of rotating machine defects by vibration analysis. Acta IMEKO 2023; 12(1): 1-6.
Dyer D, Stewart RM. Detection of rolling element bearing damage by statistical vibration analysis. (1978): 229-235.
Albezzawy MN, Nassef MG, Sawalhi N. Rolling element bearing fault identification using a novel three-step adaptive and automated filtration scheme based on Gini index. ISA transactions 2020; 101: 453-460.
Xiaodong J. A geometrical investigation on the generalized lp/lq norm for blind deconvolution. Signal Processing 2017; 134: 63-69.
Zhao M. Feature mining and health assessment for gearboxes using run-up/coast-down signals. Sensors 2016; 16(11): 1837.
Bingchang H. A comparison of machine health indicators based on the impulsiveness of vibration signals. Acoustics Australia 2021; 49: 199-206.
Ziwei Z. Bearing fault diagnosis via generalized logarithm sparse regularization. Mechanical Systems and Signal Processing 2022; 167: 108576.
Bouaouiche K. Yamina M, Dalila K. Detection and diagnosis of bearing defects using vibration signal processing. Archive of Mechanical Engineering 2023; 70(3):433–452.
Bouaouiche K. Menasria Y, Dalila K. Detection of defects in a bearing by analysis of vibration signals. Diagnostyka 2023; 24(2).
Jain, PH, Bhosle SP. A review on vibration signal analysis techniques used for detection of rolling element bearing defects. SSRG Int. J. Mech. Eng 2021; 8: 14-29.
Huang NE. Review of empirical mode decomposition. Wavelet Applications VIII. 2001; 4391.
Nazari M, Sakhaei SM. Successive variational mode decomposition. Signal Processing 2020; 174: 107610.
Peng B. A survey on fault diagnosis of rolling bearings. Algorithms 2022; 15(10): 347.
Yongjian S, Li S. Bearing fault diagnosis based on optimal convolution neural network. Measurement 2022; 190: 110702.
Zuo L. A multi-layer spiking neural network-based approach to bearing fault diagnosis. Reliability Engineering & System Safety 2002;225:: 108561.
Altaf M. A new statistical features based approach for bearing fault diagnosis using vibration signals. Sensors 2022; 22(5): 2012.
Feldman M. Hilbert transform in vibration analysis. Mechanical systems and signal processing 2011; 25(3): 735-802.
Zhou W. Empirical fourier decomposition. arXiv preprint arXiv 2019; 1912.00414.
Yonghao M, Zhao M, Hua J. Research on sparsity indexes for fault diagnosis of rotating machinery. Measurement 2020; 158: 107733.
Hebda-Sobkowicz J, Zimroz R, Wyłomańska A. Selection of the Informative Frequency Band in a Bearing Fault Diagnosis in the Presence of Non-Gaussian Noise—Comparison of Recently Developed Methods. Applied Sciences 2020; 10(8): 2657.
Case Western Reserve University bearing database.
Bingyan C. A performance enhanced time-varying morphological filtering method for bearing fault diagnosis. Measurement 2021; 176: 109163.
Yifan L, Liang X, Zuo MJ. Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis. Mechanical Systems and Signal Processing 2017; 85: 146-161.
Yifan L, Liang X, Zuo MJ. A new strategy of using a time-varying structure element for mathematical morphological filtering. Measurement 2017; 106: 53-65.
Mert S, Dumond P, Bouchard M. University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets. Data in Brief 2023; 109327.
Tse PW, Peng YH, Yam R. Wavelet analysis and envelope detection for rolling element bearing fault diagnosis—their effectiveness and flexibilities. J. Vib. Acoust. 2001; 123(3): 303-310.
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