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Rolling Element Bearing Diagnosis Using Machinery Fault Simulator

Krishna Lok Singh

Abstract


The paper presents the diagnosis strategy for the rolling element bearing system used in machinery fault simulator (MFS). The concept is generic and it is applicable for most of the system involving bearing sub-system. The MFS details and its various maintenance strategies described. The monitoring of the rolling element bearing (REB), done with the accelerometer sensors. Acquisition of data from the sensor analog voltage to the CPU in digital format carried out using LabVIEW’s DAQmx. After acquisition the data, transferred through filters such as Low-pass, High-pass, Band-pass, Band-stop, and Smooth algorithms. Further the data analyzed using FFT (Fast Fourier Transform) and CWT (Continuous Wavelet Transform), the details along with its equations discussed in this paper. The REB fault frequencies computed using the expressions and these compared with the MFS fault bearings data. The paper presents Ball Pass Frequency Outer-race (BPFO), Ball Pass Frequency Inner-race (BPFI), Ball Pass Frequency Roller (BPFR) and lastly the combined fault. Paper demonstrates the diagnosis of a bearing through these analysis and provides a feedback to the user.

Keywords: bearing, machinery fault simulator, vibration analysis, data acquisition, data transformation

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References


Applied Vibration Analysis, Training Manual Laboratory Exercises, Spectra Quest, Inc. Richmond, Virginia, 2008.

Arul Muthukumaraswamy, Suri Ganeriwala, Diagnosis of Rolling Element Bearing Faults Using Envelope Analysis, Technote, SpectraQuest Inc., 8227 Hermitage Road, Richmond, VA 23228 USA, 2009; SQi-08C-072009.

Donald E. Bently, Charles T. Hatch, Bob Grissom, Fundamentals of Rotating Machinery Diagnostics, Bently Pressurizd Bearing Press, ISBN 0-9714081-0-6, www.bpb-co.com, 2002.

Maurice L. Adams Jr, Rotating Machinery Vibration from analysis to trouble shooting, ISBN:0-8247-0258-1, Marcel Dekker Inc. 270 Madison Avenue, New York, NY 10016, http://www.dekker.com, 2001.

Ming Liang, Iman Soltani Bozchalooi, Parameter Independent Detection of Rotating Machinery Faults, United States Patent, Patent No. US008544331B2, 2015.

Asan Gani, Mje Salami, Vibration Faults Simulation System (VFSS): A Lab Equipment to aid Teaching of Mechatronics Courses, Int J Engg Ed. 2004; 20(1), 61-69pp.

Bernad J Hamrock, and William J Anderson, Rolling Element Bearings, NASA Ref Publ 1105, 1983, reissued February 2011.

Len Gelman, Tejas H. Patel, Gabrijel Persin, Brian Murray, and Allan Thomson, Novel Technology Based on the Spectral Kurtosis and Wavelet Transform for Rolling Bearing Diagnosis, Int J Prognost Health Manag. ISSN 2153-2648, 2013; 024.

Sunil Tyagi, Wavelet Analysis And Envelope Detection For Rolling Element Bearing Fault Diagnosis A Comparative Study. Center of Marine Engineering Technology, INS Shivaji, Lonavla - 410 402, 2006.

Sawalhi N Randall R B, Semi-automated bearing diagnosis-three case studies, School of Mechanical and Manufacturing Engineering. The Univ of New South Wales, Sydney 2052, Australia.

Hai Qui, Jay Lee, Jing Lin, Gang Yu, Wavelet filter-based weak signature detection method and its application on rolling element bearing prognosis, J Sound Vibrat. 2006; 289: 1066–90p.

Yi D., McInerny S. Basic Vibration Signal Processing for Bearing Fault Detection, IEEE Trans Educ 2003; 46(1): 149–56p.

Ian Howard, A Review of Rolling Element Bearing Vibration Detection, Diagnosis and Prognosis, DSTO-RR-0013, Aeronautical and Maritime Research Laboratory, Airframe and Engine Division, GPO Box 4331, Melbourne Victoria 3001 Australia, 1994.

Lisa Serir, Emmanuel Ramasso, Noureddine Zerhouni, An Evidential Evolving Prognostic Approach and its Application to PRONOSTIA’s Data Streams, Annual Conf Prognost Health Mgt Society, 2012; 302–10pp.

Endevco Corporation, 30700 Rancho Viejo Road, San Juan Capistrano, CA92675 USA, Meggitt, www.endevco.com

Marine Dumont, Andy Cook, Norton Kinsley, Acceleration Measurement Optimization: Mounting Considerations and Sensor Mass Effect, Engg Lab Supervisor, Kistler Instru Corp. 75 John Glenn Drive, Amherst, NY 14228-2171 USA, 2016.

Chance Elliott, Vipin Vijayakumar, Wesley Zink, Richard Hansen, National Instruments LabVIEW: A Programming Environment for Laboratory Automation and Measurement, J Associ Lab Automat (JALA), Tech Brief, Sage J, doi:10.1016/j.jala.2006.07.012., 2006; 12:17-24pp.

Paweł Jastrzębski, Novel Digital Signal Processing Techniques for Damage Diagnosis, School of Engg, Cranfield Univ, Cranfield, Bedfordshire, MK43 0AL, UK, 2012

Martinez W.L., Martinez A.R. Computational Statistics Handbook with MATLAB, Chapman & Hall/CRC, USA; 2001.

MATLAB R2015b, Signal Processing Tool box, www.mathworkscom.

Brodtkorb P.A., Johannesson P., Lindgren G., et al. WAFO – a Matlab toolbox for analysis of random waves and loads. Proced 10th Intl Offshore Pol Engg Conf. Seattle, 3:343–50pp.

Sreenuch T., Tsourdos, Jennions I.K. Distributed Embedded Condition Monitoring Systems Based On OSA-CBM Standard, Comput Stand Interf. 2013; 35(2):238–46pp.

Gelman L., Petrunin I., Jennions I.K., Diagnostics of local tooth damage in gears by the wavelet technology, Intl J Progn Health Manag. ISSN 2153-2648, 2012.




DOI: https://doi.org/10.37628/jsmfe.v2i1.159

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