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Volume 2, September 2014, Pages 1-30

 

Comprehensive bearing condition monitoring algorithm for incipient fault detection using acoustic emission  

Amit R. Bhende^a, Gajanan K. Awari^b, Sachin P. Untawale^c

^a Mechanical Engineering Department, SVPCET, RTM Nagpur University, Gavasi Manapur, Wardha Road, Nagpur-441108, India

^b TGPCET, RTM Nagpur University, Kh. No. 8/1, Mohgaon, Wardha Road Nagpur, Maharashtra-441108, India

^c DMIETR, RTM Nagpur University, Salod (Hirapur), Wardha-442004, India

 

 

Abstract

The bearing reliability plays major role in obtaining the desired performance of any machine. A continuous condition monitoring of machine   is required in certain applications where failure of machine leads to loss of production, human safety and precision. Machine faults are often linked to the bearing faults. Condition monitoring of machine involves continuous watch on the performance of bearings and predicting the faults of bearing before it cause any adversity. This paper investigates an experimental study to diagnose the fault while bearing is in operation. An acoustic emission technique is used in the experimentation. An algorithm is developed to process various types of signals generated from different bearing defects. The algorithm uses time domain analysis along with combination low frequency analysis technique such as fast Fourier transform and high frequency envelope detection. Two methods have adopted for envelope detection which are Hilbert transform and order analysis. Experimental study is carried out for deep groove ball bearing cage defect. Results show the potential effectiveness of the proposed algorithm to determine presence of fault, exact location and severity of fault. 

 

 

Keywords

Bearing; Time domain; Frequency Domain; Envelope analysis; Hilbert transform  

 

 

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References

Bo, T. Limin, Z. Han, D. and Youlun, X., 2007, An alternative time-domain index for condition monitoring of rolling element bearings—A comparison study. Elsevier, Reliability Engineering and System Safety, 92, 660–670.

Changting, W. and Robert X. G., 2000, A Virtual Instrumentation System for Integrated Bearing Condition Monitoring. IEEE Transaction on Instrumentation and Measurement, 49, 2, 325-332.

Diagnostic Maintenance and Monitoring of Machines, Chapter – 8 Machine Condition Indicators, Indian Institute of Technology, Roorkee.

Dron, J. P., Bolaers, F. and Rasolofondraibe, I., 2004, Improvement of the sensitivityof the scalar indicators (crest factor, kurtosis) using a de-noising method byspectral subtraction: application to the detection of defects in ball bearings. Journal of Sound and Vibration, 270, 61–73.

Eric, B. Michael, K. and Praneet, M., 2011, Bearing Envelope Analysis Window Selection Using Spectral Kurtosis Techniques.

Fabio Immovilli, Marco Cocconcelli, Alberto Bellini and Riccardo Rubini,2009, Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals. IEEE Transactions on Industrial Electronics, vol. 56, no. 11.

Guillermo, A. J., Alfredo, O. M. and Manuel, A. D., 2007, Fault detection in induction motors using Hilbert and Wavelet transforms. Elecrical Engineering, 89, 205–220.

Howard, I., 1994, A Review of Rolling Element Bearing Vibration: Detection, Diagnosis and Prognosis. DSTO-RR-0013, Defence science and technology organisation Canberra (Australia).

Kumar, P. and Manuhar, V. H., 2003, Comprehensive Predictive Maintenance of electrical motors in Indian nuclear power plants. International Journal of Nuclear Power, 17.

Lorenzo, F. and Calabro, M.,2007, Kurtosis: A statistical approach to identify defect in rolling bearings.

Manish, Y. and Sulochana W., 2011, Vibration analysis of bearing for fault detection using time  domain features and neural network. International Journal of Applied Research in Mechanical Engineering, Volume-1, Issue-1, pp. 69-74.

McFadden, P. D., and Smith, J. D., 1984, Vibration Monitoring of Rolling Element Bearings by the High Frequency Resonance Technique—A Review. Tribol. Int., 17,1, 3–10.

McInerny, S. A. and Dai, Y., 2003, Basic Vibration Signal Processing for Bearing Fault Detection. IEEE Transactions on Education, vol. 46, no. 1.

Mobley, R. K., 1999, Vibration Fundamentals. Elsevier.

Mustapha, M. Pierre-Philippe, J. B. and David, J. V., 2011, Comparison of Fault Detection Techniques for an Ocean Turbine. Annual Conference of the Prognostics and Health Management Society.

National Instruments, LabviewTM 2012, Contex Help.

Petr, F., 2009, Theoretical verification of envelope analysis of rolling element bearings with using Matlab.

Prasad, H., Ghosh, M., and Biswas, S., 1985, Diagnostic Monitoring of Rolling Element Bearings by High Frequency Resonance Technique. ASLE Trans., 28, 4, 439–448.

Proakis, J. G. and Manolakis, D. G., 1997, Digital Signal Processing. Third Edition, Prentice Hall India Pvt. Ltd.

Proakis, J. G. and Salehi, M., 2000, Contemporary Communication Systems. BookWare Companion Series.

Radoslav, T., Vojislav, M., Milan, B. and Aleksandar, M., 2010, Vibration response of rigid rotor in unloaded rolling element bearing.  International Journal of Mechanical Sciences, 52, 1176-1185.

Singh S. K., 2007, Acoustics based condition monitoring. Technical Paper, IIT Guwahati.

Spyridon, G. M. and Ioannis, C., 2009, Study and construction of an apparatus that automatically monitors vibration and wears in radial ball bearings which are loaded in radial direction. International Conference on Signal Processing Systems, IEEE Computer Society,  292-296.

Sunil, T., 2010, Wavelet Analysis And Envelope Detection For Rolling Element Bearing Fault Diagnosis . A Comparative Study. Center of Marine Engineering Technology, INS Shivaji, Lonavla 410402.

Sylvester, A. A., 2011, Statistical approach for tapered bearing fault detection using different methods. Proceedings of the World Congress on Engineering 2011 Vol III WCE 2011, July 6 - 8, London, U.K.

Tandon, N. and Choudhary, A., 1999, A Review of Vibration and Acoustic Measurement Methods for Detection of Defects in Rolling Element Bearing. Tribology International, 38(8), pp. 469-480.

Vassa, J., Mid, R. S., Randall, R. B., Sovka, P., Cristalli, C. and Torcianti B.,2008, Avoidance of speckle noise in laser vibrometry by the use of kurtosis ratio: Application to mechanical fault diagnostics. Elsevier, Mechanical Systems and Signal Processing, 22, 647–671.

Yong-Han, K. Andy, C. Tan, C. Joseph, M. and Bo-Suk, Y., 2006, Condition monitoring of low speed bearings: A comparative study of the ultrasound technique versus vibration measurements. WCEAM 2006 Paper 029, 1-10.

Yuan, Y. and Zhang, Z., 2010, Fault Diagnosis of Rolling Rearing Based on the Wavelet Analysis. 2nd International Asia Conference on Informatics in Control, Automation and Robotics, IEEE, 257-260.