The importance of FFT and BCS spectrums analysis for diagnosis and prediction of rolling bearing failure

• Autorzy: Nadabaică D. C. , Nedeff V. , Radkowski S. , Mączak J.
• Języki publikacji: EN

Abstrakty

EN

Dynamic equipments, in their vast majority, have rolling bearings in their components. Measurement and analysis of the values of rolling bearings vibration, on time, represent a safe and effective measure for identifying the state of wear of bearings, and to predict the evolution of their technical condition and of the entire equipment. This paper presents the detection of causes which lead to the damage of rolling bearing by using FFT (Fast Fourier Transformation) and BCS (Bearing Condition Signature) spectrums, by measuring its housing vibrations (self-aligning ball bearing, ZKL 1205K type) mounted in a test rig. The results presents the analysis mode of FFT and BCS spectrums in order to obtain beneficial information for the detection of that unbalance caused by loading, of the implemented defect on rolling bearing raceway and of bearing damage mode under the action of these causes.

Słowa kluczowe

EN

FFT spectrum; BCS spectrum; rolling bearings; diagnose; prediction

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2013
• Tom: Vol. 14, No. 4
• Strony: 3--12
• Opis fizyczny: Bibliogr. 10 poz., rys., tab., wykr.

Twórcy

autor

Nadabaică D. C.

• “Vasile Alecsandri” University of Bacau, Department of Environmental Engineering and Mechanical Engineering, Calea Marasesti 156, Bacau, 600115, Romania

autor

Nedeff V.

• “Vasile Alecsandri” University of Bacau, Department of Environmental Engineering and Mechanical Engineering, Calea Marasesti 156, Bacau, 600115, Romania

autor

Radkowski S.

• Warsaw University of Technology, Institute of Vehicles, Narbutta 84, 02-524 Warsaw, Poland

autor

Mączak J.

• Warsaw University of Technology, Institute of Vehicles, Narbutta 84, 02-524 Warsaw, Poland

Bibliografia

• [1] Orhan S., Akturk N., Celik V.: Vibration monitoring for defect diagnosis of rolling element bearings as a predictive maintenance tool: Comprehensive case studies, NDT&E International, 2006, Vol. 39, No. 4, pp. 293-298.
• [2] Băjenaru S., Ganga M., tin V., Danciu A.: The study of the diagnosis theory applied for predictive maintenance, Research Journal of Agricultural Science, 2009, Vol. 41, No. 2, pp. 338-350.
• [3] Balerston H. L.: The Detection of Incipient Failure in Bearings, Materials Evaluation, 1996, Vol. 27, pp. 121 - 128.
• [4] Tse P., Peng Y., Yam R.: Wavelet analysis and envelope detection for rolling element bearing fault diagnosis their effectiveness and flexibilities, Journal of Vibration and Acoustics, 2001, Vol. 123, pp. 303-310.
• [5] Mobley K. R.: Root Cause Failure Analysis, Reed Elsevier, USA, 1999.
• [6] Nadabaica D. C., Bibire L., Andrioai G.: Study of the advantages of predictive maintenance in the monitoring of rolling bearings, Environmental Engineering and Management Journal, 2012, Vol. 11, No. 12, pp 2233-2238.
• [7] Ragulskis K., Yurkauskas A.: Vibration of Bearing, Hemisphere Publishing Corporation, Bristol, 1989.
• [8] Rieger N. F., Crofoot J. F.: Vibration of Rotationary Machinary, Rochester Institute of Tehnology, 1977, pp.69-77.
• [9] Al-Hazmi M.W.: The Effect Of The Manufacturing Errors On The Dynamic Performance Of Gears, Journal of Engineering & Architecture, 2011, Vol. 4, No. 2, pp. 37-53.
• [10] Dennis H. S.: Signal Processing For Effective Vibration Analysis, IRD Mechanalysis, Inc. Columbus, Ohio, 1995.