Detection of defects in a bearing by analysis of vibration signals
1
Electromechanical engineering laboratory, Badji Mokhtar-Annaba University,
Submission date: 2022-12-09
Final revision date: 2023-01-26
Acceptance date: 2023-03-14
Online publication date: 2023-03-21
Publication date: 2023-03-21
Corresponding author
Diagnostyka 2023;24(2):2023203
KEYWORDS
TOPICS
ABSTRACT
This work presents the analysis of vibration signals by an approach consists of several mathematical tools more elaborate such as the Hilbert transform, kurtogram, which allows the detection of vibration defects in a simple and accurate way. The steps or methods inserted in the process one complementary to the other as scalar indicators generally used in monitoring to follow the evolution of the functioning of a machine when an abnormal functioning it must make a diagnosis to detect the failing element through the use of a process. The determination of the defective organs at an optimal time is a very important operation in the industrial maintenance, which keeps the equipment in a good condition and ensures the assiduity of work. To see the effectiveness of fault detection by the proposed approach by analyzing the real vibration signals of a bearing type 6025-SKF available on the Case Western Reserve University platform.
REFERENCES (18)
1.
Mohd Ghazali MH, Rahiman W. Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic review. Shock and Vibration. 2021; 2021:9469318. https://doi.org/10.1155/2021/9....
2.
Gundewar SK, Kane PV. Condition monitoring and fault diagnosis of induction motor. Journal of Vibration Engineering & Technologies. 2021;9(4): 643-674. https://doi.org/10.1007/s42417....
3.
Ramadhan S, Susanto H. Analisa kerusakan conveyor pada pt mifa bersaudara dengan metode reability centered maintenance. Jurnal Mahasiswa Mesin. 2022; 1(2): 10-17.
4.
Randall RB. State of the Art in Monitoring Rotating Machinery - Part 1. Sound & Vibration. 2004; 38(3): 14-21.
5.
Avoci Ugwiri M, Carratù M, Lay-Ekuakille A, Paciello V, Pietrosanto A. Cascade based methods in detecting rotating faults using vibration measurements. 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE. 2021:1-5. https://doi.org/10.1109/I2MTC5....
6.
Lin H-C, Ye Y-C, Huang B-J, Su J-L. Bearing vibration detection and analysis using enhanced fast Fourier transform algorithm. Advances in Mechanical Engineering. 2016; 8(10): 1687814016675080. https://doi.org/10.1177/168781....
7.
Amanuel T, Ghirmay A, Ghebremeskel H, Ghebrehiwet R, Bahlibi W. Comparative Analysis of Signal Processing Techniques for Fault Detection in Three Phase Induction Motor. Journal of Electronics. 2021; 3(1):61-76. https://doi.org/10.36548/jei.2....
8.
Torres ME, Colominas MA, Schlotthauer G, Flandrin P. A complete ensemble empirical mode decomposition with adaptive noise. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2011: 4144-4147. https://doi.org/10.1109/ICASSP....
9.
Hoseinzadeh MS, Khadem SE, Sadooghi MS. Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition. ISA Transactions. 2018;83:261-275. https://doi.org/10.1016/j.isat....
10.
Jain PH, Bhosle SP. Study of effects of radial load on vibration of bearing using time-Domain statistical parameters. IOP Conference Series: Materials Science and Engineering. 2021; 1070(2021): 012130. http://doi.org/10.1088/1757-89....
11.
Chaudhari H, Nalbalwar SL, Sheth R. A review on intrensic mode function of EMD. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). 2016: 2349-2352. http://doi.org/10.1109/ICEEOT.....
12.
Xiaozhou D, Qi X, Haiyang G, Zihong Z. A Method of On-line Monitoring for Vibration Table Bearings Based on VMD. IOP Conference Series: Materials Science and Engineering. 2020; 751(2020): 012017. https://doi.org/10.1088/1757-8....
13.
Mahgoun H, Bekka RE, and FELKAOUI, Ahmed. Gearbox fault diagnosis using ensemble empirical mode decomposition (EEMD) and residual signal. Mechanics & Industry. 2012; 13(1): 33-44. https://doi.org/10.1051/meca/2....
14.
Al-Dabag MLA, Al Rikabi H, Al-Nima RRO. Anticipating Atrial Fibrillation Signal Using Efficient Algorithm. International Association of Online Engineering. 2023. https://www.learntechlib.org/p....
15.
Antoni J. Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing. 2007; 21(1): 108-124. https://doi.org/10.1016/j.ymss....
16.
Jiménez GA, Muñoz AO, Duarte-Mermoud MA. Fault detection in induction motors using Hilbert and Wavelet transforms. Electrical Engineering. 2007; 89(3): 205-220. https://doi.org/10.1007/s00202....
18.
Neupane D, Seok J. Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review. IEEE Access. 2020; 8: 93155-93178.
Share
RELATED ARTICLE
| eISSN: | 2449-5220 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
