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Control and prediction protocol for bearing failure through spectral power density

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper aims to analyse the results of the comparative study of the characteristic frequencies, in terms of Power Spectral Density (PSD), generated by an SKF6322 bearing in a rotational blower. Among all the analysed frequencies, we have focused on the ones generated by the shaft rotation speed, the one on the blades and the ones of the SKF6322 bearing, such as the tracks, the cage and the balls. For this study, we followed the ISO 10816 criteria, both in the sampling part and in the data analysis, using the speed values in terms of PSD, which improves the results in both high and low frequencies. This study can be used to predict the performance of bearings and their future failure, determining the most decisive frequency, the one with the highest incidence and the relative influence of each one on the different positions and monitoring coordinate axes. This procedure can be applied to improve the predictive maintenance protocol in order to improve the performance, efficiency and reliability of the equipment with bearings in their systems.
Słowa kluczowe
Rocznik
Strony
651--657
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
  • Department of Mining, Mechanical, Energy and Construction Engineering, Higher Technical School of Engineering, University of Huelva, 21007 Huelva, Spain
  • Department of Mining, Mechanical, Energy and Construction Engineering, Higher Technical School of Engineering, University of Huelva, 21007 Huelva, Spain
  • Department of Mining, Mechanical, Energy and Construction Engineering, Higher Technical School of Engineering, University of Huelva, 21007 Huelva, Spain
Bibliografia
  • 1. Artzer A, Moats M, Bender J. Removal of Antimony and Bismuth from Copper Electrorefining Electrolyte: Part I—A Review. The Journal of The Minerals, Metals & Materials Society 2018; 70: 2033– 2040, https://doi.org/10.1007/s11837-018-3075-x.
  • 2. Bo Sun, Mengmeng Li, Baopeng Liao, Xi Yang, Yitong Cao, Bofeng Cui, Qiang Feng, Yi Ren, Dezhen Yang. Time-Variant Reliability modeling based on hybrid non-probability method. Archive of Applied Mechanics, 2019, 90(2): 209-219, https://doi.org/10.1007/s00419-019-01605-1.
  • 3. Castilla-Gutiérrez J, Fortes JC, Pulido-Calvo I. Analysis, evaluation and monitoring of the characteristic frequencies of pneumatic drive unit and its bearing through their corresponding frequency spectra and spectral density. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2019; 21 (4): 585–591, http://dx.doi.org/10.17531/ein.2019.4.7.
  • 4. Chaudhry V, Kailas S V. Elastic-Plastic Contact Conditions for Frictionally Constrained Bodies Under Cyclic Tangential Loadin. Journal of Tribology 2013; 136 (1), https://doi.org/10.1115/1.4025600.
  • 5. Chudzik A, Warda B. Effect of radial internal clearance on the fatigue life of the radial cylindrical roller bearing. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2019; 21 (2): 211–219, http://dx.doi.org/10.17531/ein.2019.2.4.
  • 6. Cong F, Chen G, Dong G. Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis. Journal of Sound and Vibration 2013; 332 (8): 2081–2097, https://doi.org/10.1016/j.jsv.2012.11.029.
  • 7. Ding X, He Q, Luo N. A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification. Journal of Sound and Vibration 2015; 335: 367–383, https://doi.org/10.1016/j.jsv.2014.09.026.
  • 8. Goyal D, Choudhary A, Pabla B.S. Support vector machines based non-contact fault diagnosis system for bearings. Journal of Intelligent Manufacturing 2019; 1572-8145, https://doi.org/10.1007/s10845-019-01511-x
  • 9. Gowid S, Dixon R, Ghandi S. Characterisation of Major Fault Detection Features and Techniques for the Condition-Based Monitoring of Highspeed Centrifugal Blowers. International Journal of Acoustics and Vibration 2016; 21 (2), http://dx.doi.org/10.20855/ijav.2016.21.2410.
  • 10 Houpert L. An Enhanced Study of the Load–Displacement Relationships for Rolling Element Bearings. Journal of Tribology 2013; 136: 011105, https://doi.org/10.1115/1.4025602.
  • 11 Huang L, Huang H, Liu Y. A Fault Diagnosis Approach for Rolling Bearing Based on Wavelet Packet Decomposition and GMM-HMM. International Journal of Acoustics and Vibration 2019; 24 (2), 199-209, https://doi.org/10.20855/ijav.2019.24.21120.
  • 12 Ise T, Osaki M, Matsubara M, & Kawamura S. Vibration Reduction of Large Unbalanced Rotor supported by Externally Pressurized Gas Journal Bearings with Asymmetrically Arranged Gas Supply Holes (Verification of the Effectiveness of a Supply Gas Pressure Control System). Journal of Tribology 2019; 141 (3): 031701, https://doi.org/10.1115/1.4041460.
  • 13 Kausschinger B, Schroeder S. Uncertainties in Heat Loss Models of Rolling Bearings of Machine Tools, Procedia CIRP 2016; 46: 107 – 110, https://doi.org/10.1016/j.procir.2016.03.168.
  • 14 Li H, Fu L, Zheng H. Bearing fault diagnosis based on amplitude and phase map of Hermitian wavelet transform. Journal of Mechanical Science and Technology 2011; 25 (11): 2731– 2740, https://doi.org/10.1007/s12206-011-0717-0.
  • 15 Li Y, Billington S, Zhang C, Kurfess T, Danyluk S, & Liang S. Adaptive prognostics for rolling element bearing condition. Mechanical Systems and Signal Processing 1999; 13 (1), 103–113, https://doi.org/10.1006/mssp.1998.0183.
  • 16 Louhichi R, Sallak M, and Pelletan J. A Maintenance Cost Optimization Approach: Application on a Mechanical Bearing System. International Journal of Mechanical Engineering and Robotics Research 2020; 9 (5): 658-664, https://doi.org/10.18178/ijmerr.9.5.658-664.
  • 17 Madoliat R, Ghanati M F. Theoretical and Experimental Study of Spindle Ball Bearing Nonlinear Stiffness. Journal of Mechanics 2013; 29:633-642, https://doiorg/101017/jmech201348.
  • 18 Malla C, Panigrahi I. Review of Condition Monitoring of Rolling Element Bearing Using Vibration Analysis and Other Techniques Journal of Vibration Engineering & Technologies 2019; 7: 407–414, https://doi.org/10.1007/s42417-019-00119-y.
  • 19 Medina S, Olver A V, Dini D. The Influence of Surface Topography on Energy Dissipation and Compliance in Tangentially Loaded Elastic Contacts. Journal of Tribology 2012; 134 (1), https://doi.org/10.1115/1.4005641.
  • 20 Medrano-Hurtado Z Y, Medrano-Hurtado C, Pérez-Tello J, Gómez Sarduy M, Vera-Pérez N. Methodology of Fault Diagnosis on Bearings in a Synchronous Machine by Processing Vibro-Acoustic Signals Using Power Spectral Density Ingeniería. Investigación y Tecnología 2016; 17 (1): 73-85, https://doi.org/10.1016/j.riit.2016.01.007.
  • 21 Nagi G, Alaa E, Jing P. Residual Life prediction sin the absence of prior degradation know ledge. IEEE Trans. Reliab. 2009; 58: 106–116, https://doi.org/10.1109/TR.2008.2011659.
  • 22 Nandi S, Toliyat H A, Li X. Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review. IEEE Transactions on Energy Conversion 2005; 20 (4): 719–729, https://doi.org/10.1109/TEC.2005.847955.
  • 23 Omoregbee H O, Heyns P S. Fault Classification of Low-Speed Bearings Based on Support Vector Machine for Regression and Genetic Algorithms Using Acoustic Emission. J. Vib. Eng. Technol 2019; 7: 455–464, https://doi.org/10.1007/s42417-019-00143-y
  • 24 Orhan S, Aktürk N, Celik V. Vibration monitoring for defect diagnosis of rolling element bearings as a predictive maintenance tool: Comprehensive case studies. NDT & E International 2006; 39 (4): 293-298, https://doi.org/10.1016/j.ndteint.2005.08.008
  • 25 Pawlik P. Single-number statistical parameters in the assessmente of the techinical condition of machines operating under variable load. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2019; 21 (1): 164-169, http://ds.doi.org/10.17531/ein.2019.1.19.
  • 26 Polimac V, Polimac J. Assessment of present maintenance practices and future trends. IEEE/PES Transmission and Distribution Conference and Exposition. Developing New Perspectives 2001; 2: 891-894, http://10.1109/TDC.2001.971357
  • 27 Schnabel S, Marklund P, Larsson R, Golling S. The Detection of Plastic Deformation in Rolling Element Bearings by Acoustic Emission. Tribiology International 2017; 110: 209-201, https://doi.org/10.1016/j.triboint.2017.02.021.
  • 28 Toledo E, Pinhas I, Aravot D, Akselrod S. Bispectrum and bicoherence for the investigation of very high frecuency peaks in heart rate variability. Proceedings of the IEEE, Computers in Cardiology 2001; 28: 667-670, https://doi.org/10.1109/CIC.2001.977744.
  • 29 Wang J, Liang Y, Zheng Y, Gao, Zhang F. An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples. Renewable Energy 2020; 145: 642-650, https://doi.org/10.1016/j.renene.2019.06.103
  • 30 Wang N F, Jiang1 D X, Yang W G Dual‑Tree Complex Wavelet Transform and SVD‑Based Acceleration Signals Denoising and its Application in Fault Features Enhancement for Wind Turbine. Journal of Vibration Engineering & Technologies 2019; 7: 311–320, https://doi.org/10.1007/s42417-019-00126-z.
  • 31 Zheng D, Chen W. Thermal performances on angular contact ball bearing of high- speed spindle considering structural constratints under oil-air lubrication. Tribology International 2017; 109: 593–601 9, https://doi.org/10.1016/j.triboint.2017.01.035.
  • 32 Zhou W, Habetler T G, Harley R G. Bearing Condition Monitoring Methods for Electric Machines: A General Review. IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives 2007; 3-6, https://doi.org/10.1109/demped.2007.4393062.
  • 33 Zuber N, Bajric R. Application of artificial neural networks and principal component analysis on vibration signals for automated fault classification of roller element bearings. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2016; 18 (2): 299–306, http://dx.doi.org/10.17531/ein.2016.2.19.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3ddc4629-4b8a-4f3f-8c94-d1cbda58dc8e
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