Detection of defects in a bearing by analysis of vibration signals

• Autorzy: Bouaouiche Karim , Menasria Yamima , Khalifa Dalila

Identyfikatory

DOI

10.29354/diag/162230

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

vibration signal; Hilbert transform; bearing; kurtogram; detection

PL

sygnał drganiowy; transformata Hilberta; łożysko; kurtogram

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2023
• Tom: Vol. 24, No. 2
• Strony: art. no. 2023203
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Bouaouiche Karim

• Electromechanical engineering laboratory, Badji Mokhtar University, Annaba, Algeria

autor

Menasria Yamima

• Electromechanical engineering laboratory, Badji Mokhtar University, Annaba, Algeria

autor

Khalifa Dalila

• Electromechanical engineering laboratory, Badji Mokhtar University, Annaba, Algeria

Bibliografia

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• 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/I2MTC50364.2021.9460107.
• 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/10.1177/1687814016675080.
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• 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.isatra.2018.09.008.
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• 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/10.1088/1757-899X/751/1/012017.
• 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/2011150.
• 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/218994.
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• 17. Database Case Western Reserve University, https://engineering.case.edu/bearingdatacenter/downl oad-data-file.
• 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.

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).