A Study of Motor Bearing Fault Diagnosis using Modulation Signal Bispectrum Analysis of Motor Current Signals

Ahmed Alwodai, Tie Wang, Zhi Chen, Fengshou Gu, Robert Cattley, Andrew Ball

Centre for Efficiency and Performance Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
Department of Vehicle Engineering, Taiyuan University of Technology Taiyuan, Shanxi Province, China.

DOI: 10.4236/jsip.2013.43B013                

Abstract

Failure of induction motors are a large concern due to its influence over industrial production. Motor current signature analysis (MCSA) is common practice in industry to find motor faults. This paper presents a new approach to detection and diagnosis of motor bearing faults based on induction motor stator current analysis. Tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high nose levels, a modulation signal bispectrum (MSB) is used in this paper to detect and diagnose different motor bearing defects. The results show that bearing faults can induced a detestable amplitude increases at its characteristic frequencies. MSB peaks show a clear difference at these frequencies whereas conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in extract small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also show that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component.

Keywords

Induction Motor; Motor Current Signature; Power Spectrum Bispectrum; Motor Bearing

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	D. Basak, A. Tiwari and S. P. Das, “Fault Diagnosis and Condition Monitoring of Electrical Machines—A Review,” in Proc. IEEE ICIT, 2006, pp. 3061–3066.
[2]	M. E. H. Benbouzid, “A Review of Induction Motors Signature Analysis as a Medium for Faults Detection,” IEEE Transactions Industrial Electron, Vol. 47, No. 5, 2000, pp. 984–993.doi:10.1109/41.873206
[3]	M. Blödt, P. Granjon, B. Raison and G. Rostaing, “Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring,” IEEE Transactions On Industrial Electronics Vol. 55, No. 4, 2008, pp. 1813-1822.doi:10.1109/TIE.2008.917108
[4]	A. Shrivastava and S. Wadhwani, “Condition Monitoring for Inner Raceway Fault of Induction Motor Ball Bearings,”International Journal of Electrical Engineering, Vol. 5, 2012, pp. 239-244.
[5]	J. Stack, T. G. Habetler and R. G. Harley, “Fault ClassiFication and Fault Signature Production for Rolling Element Bearings in Electric Machines,” IEEE Transactions Industrial Applications, Vol. 40, 2004, PP. 735–739.
[6]	R. R. Schoen, T. G. Habetler, F. Kamran and R. Bartheld, “Motor Bearing Damage Detection Using Stator Current Monitoring,” IEEE Transactions Industrial Applications, Vol. 31, No. 6, 1995, pp. 1274–1279. doi:10.1109/28.475697
[7]	R. R. Obaid, T. G. Habetler and J. R. Stack, “Stator Current Analysis for Bearing Damage Detection in Induction Motors,”in Proc. SDEMPED,Atlanta, 2003, pp. 182–187.
[8]	I. Rodríguez and R. Alves, “Bearing Damage Detection of the Induction Motors Using Current Analysis,”in Proc. TDC IEEE/PES Transmiss.Distrib. Conf. Expo.: Latin America, August 2006, pp. 1–5.
[9]	L. Frosini and E. Bassi, “Stator Current and Motor Efficiency as Indicators for Different Types of Bearing Faults in Induction Motors,” IEEE Transactions on Industrial electronics, Vol. 57, No. 1, 2010, pp. 244-251. doi:10.1109/TIE.2009.2026770
[10]	G. C. Zhang, M. G. e, H. Tong, Y. Xu and R. Du, “Bispectral Analysis for on-line Monitoring of Stamping Operation,”Engineering Applications of Artificial Intelligence, Vol. 15, No. 1, 2002, pp. 97–104. doi:10.1016/S0952-1976(02)00007-6
[11]	W. B. Collis, P. R. White and J. K. Hammond, “Higher-order Spectra: The Bispectrum and Trispectrum,” Mechanical Systems and Signal Processing, Vol. 12, No. 3, 1998, pp. 375-394. doi:10.1006/mssp.1997.0145
[12]	J. W. A. Fackrell, S. McLaughlin and P. R. White, “Bicoherence Estimation Using the Direct Method. Part 1: Theoretical considerations,”Applications. Signal Process,Vol. 3,1995, pp. 155-168.
[13]	F. Gu, Y. Shao, N. Hu and A. D. Ball, “Electrical Motor Current Signal Analysis Using a Modified Bispectrum for Ault Diagnosis of Downstream Mechanical Equipment,” Mechanical Systems and Signal Processing,” Vol. 25, No. 1,2011, pp. 360–372. doi:10.1016/j.ymssp.2010.07.004
[14]	A. Alwodai, X. Yuan, Y. Shao, F. Gu, and A. D. Ball, “Modulation Signal Bispectrum Analysis of Motor Current Signals for Stator Fault Diagnosis,” Proceedings of the 18th ICAC, September 2012.