Skip to main content
SpringerLink
Log in

An Intelligent Fault Diagnosis of Induction Motors in an Arbitrary Noisy Environment

  • Published:
Journal of Nondestructive Evaluation Aims and scope Submit manuscript

Abstract

In this paper, an intelligent fault diagnosis system based on instantaneous power spectrum analysis is proposed. The instantaneous noise variations and sensor off-sets are considered to be one of the common factors that yield erroneous fault tracking in an online condition monitoring and fault diagnosis system. The developed system has the capability to detect bearing inner race defects at incipient stages with in an arbitrary noise conditions. An adaptive threshold has been designed to deal with line current noise ambiguities for decision-making on the existence of small fault signatures. The performance of the developed system has been analyzed theoretically and experimentally on a custom designed test rig under various loading conditions of the motor.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Abbreviations

ANN:

Artificial neural network

CM:

Condition monitoring

DSP:

Digital signal processing

DAQ:

Data acquisition

IPA:

Instantaneous power analysis

IM:

Induction motors

MCSA:

Motor current signature analysis

PVA:

Park vector analysis

USB:

Upper side band

LSB:

Lower side band

EPRI:

Electric power research institute

FFT:

Fast Fourier transform

RPM:

Revolution per mintue

References

  1. Immovilli, F., Bianchini, C., Cocconcelli, M., Bellini, A., Rubini, R.: Bearing fault model for induction motor with externally induced vibration. IEEE Trans. Ind. Electron. 60(8), 3408–3418 (2013)

    Article  Google Scholar 

  2. Ozturk, H., Yesilyurt, I., Sabuncu, M.: Detection and advancement monitoring of distributed pitting failure in gears. J. Nondestruct. Eval. 29(2), 63–73 (2010)

    Article  Google Scholar 

  3. IAS Motor Reliability Working Group, Report of large motor reliability survey of industrial and commercial installations—Part III. In: IEEE Transactions on Industry Applications, vol. IA-23, pp. 153–158, Jan/Feb 1987

  4. EPRI Publication EL-2678, Improved motors for utility applications, vol. 5, Oct 2005

  5. Tavner, P.J., Ran, L., Pennman, J., Sedding, H.: Condition Monitoring of Rotating Electrical Machines. Research Studies Press Ltd., Letchworth (2008)

    Book  Google Scholar 

  6. Choi, S., Pazouki, E., Baek, J., Bahrami, H.R.: Iterative condition monitoring and fault diagnosis scheme of electric motor for harsh industrial application. IEEE Trans. Ind. Electron. 62, 1760–1769 (2014). doi:10.1109/TIE.2014.2361112

    Article  Google Scholar 

  7. Shi, P., Chen, Z., Vagapov, Y.: Modelling and analysis of induction machines under broken rotor-bar failures. Int. J. Comput. Appl. 69(14), 28–35 (2013)

    Google Scholar 

  8. Choi, S., Akin, B., Kwak, S., Toliyat, H.: A compact error management algorithm to minimize false-alarm rate of motor/generator faults in (hybrid) electric vehicles. IEEE J. Emerg. Sel. Top. Power Electron. 2, 618–626 (2014). doi:10.1109/JESTPE.2014.2302902

    Article  Google Scholar 

  9. Choi, S.D., Akin, B., Rahimian, M., Toliyat, H.A.: Implementation of fault diagnosis algorithm for induction machines based on advanced digital signal processing techniques. IEEE Trans. Ind. Electron. 58(3), 937–948 (2011)

    Article  Google Scholar 

  10. Akin, B., Choi, S.D., Orguner, U., Toliyat, H.A.: A simple real-time fault signature monitoring tool for motor drive imbedded fault diagnosis systems. IEEE Trans. Ind. Electron. 58(5), 1990–2001 (2011)

    Article  Google Scholar 

  11. Blodt, M., Granjon, P., Raison, B., Rostaing, G.: Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans. Ind. Electron. 55(4), 1813–1822 (2008)

    Article  Google Scholar 

  12. Stack, J.R., Habetler, T.G., Harley, R.G.: Fault classification and fault signature production for rolling element bearing in electric machines. IEEE Trans. Ind. Appl. 40(3), 735–739 (2004)

    Article  Google Scholar 

  13. Benbouzid, M.E.H., Nejjari, H., Beguenane, R., Vieira, M.: Induction motor asymmetrical faults detection using advanced signal processing techniques. IEEE Trans. Energy Convers. 14(2), 147–152 (1999)

    Article  Google Scholar 

  14. Patel, V.N., Tandon, N., Pandey, R.K.: Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and Duffing oscillator. Measurement 45, 960–970 (2012)

    Article  Google Scholar 

  15. Kang, M., Kim, J., Kim, J.-M.: Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm. Inf. Sci. 294, 423–438 (2015)

    Article  MathSciNet  Google Scholar 

  16. Choi, S.D., Akin, B., Rahimian, M.M., Toliyat, H.A., Azadpour, M.: A generalized condition monitoring method for multi-phase induction motors. In: IEEE International Conference on Electric Machines and Drives (2009)

  17. Wang, D., Miao, Q., Fan, X., Huang, H.Z.: Rolling element bearing fault detection using an improved combination of Hilbert and Wavelet transforms. J. Mech. Sci. Technol. 23, 3292–3301 (2009)

    Article  Google Scholar 

  18. Widodo, A., Yang, B.-S., Han, T.: Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Syst. Appl. 32(2), 299–312 (2007)

    Article  Google Scholar 

  19. Hwang, Y., Jen, K., Shen, Y.: Application of cepstrum and neural network to bearing fault detection. J. Mech. Sci. Technol. 23, 2730–2737 (2009)

    Article  Google Scholar 

  20. Júnior, A.M.G., Silva, V.R., Baccarini, L.M.R., Reis, M.L.F.: Three-phase induction motors faults recognition and classification using neural networks and response surface models. J Control Autom. Electr. Syst. 25(3), 330–338 (2014)

    Article  Google Scholar 

  21. Nandi, S., Toliyat, H.A., Li, X.: Condition monitoring and fault diagnosis of electrical machines—a review. IEEE Trans. Energy Convers. 20(04), 719–729 (2005)

    Article  Google Scholar 

  22. Zhou, W., Habetler, T.G., Harley, R.G.: Bearing condition monitoring methods for electric machines: a general review. In: IEEE International System Diagnostics Electric Machines & Power Electronics Drives (2007)

  23. Tanver, P.J.: Review of condition monitoring of rotating electrical machines. IET Electr. Power Appl. 02(4), 215–247 (2008)

    Article  Google Scholar 

  24. Zhang, P., Du, Y., Habetler, T.G., Lu, B.: A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans. Ind. Appl. 47(1), 34–46 (2011)

    Article  Google Scholar 

  25. Gao, Z., Cecati, C., Ding, S.X.: A survey of fault diagnosis and fault-tolerant techniques part I: fault diagnosis with modelbased and signal-based approaches. IEEE Trans. Ind. Electron. 62, 3757–3767 (2015). doi:10.1109/TIE.2015.2417501

    Article  Google Scholar 

  26. Cruz, S.M.A.: An active-reactive power method for the diagnosis of rotor faults in three-phase induction motors operating under time-varying load conditions. IEEE Trans. Energy Convers. 27(1), 71–84 (2012)

    Article  Google Scholar 

  27. Drif, M., Cardoso, A.J.M.: Airgap-eccentricity fault diagnosis, in three-phase induction motors, by the complex apparent power signature analysis. IEEE Trans. Ind. Electron. 55(3), 1404–1410 (2008)

    Article  Google Scholar 

  28. Drif, M., Cardoso, A.J.M.: Discriminating the simultaneous occurrence of three-phase induction motor rotor faults and mechanical load oscillations by the instantaneous active and reactive power media signature analyses. IEEE Trans. Ind. Electron. 59(3), 1630–1639 (2012)

    Article  Google Scholar 

  29. Drif, M., Cardoso, A.J.M.: Stator fault diagnostics in squirrel cage three-phase induction motor drives using the instantaneous active and reactive power signature analyses. IEEE Trans. Ind. Inform. 10(2), 1348–1360 (2014)

    Article  Google Scholar 

  30. Kim, J., Shi, S., Lee, S.B., Gyftakis, K.N., Drif, M., Cardoso, A.J.M.: Power spectrum-based detection of induction motor rotor faults for immunity to false alarms. In: IEEE Transactions on Energy Conversion (2015)

  31. Rajalakshmi Samaga, B.L., Vittal, K.P.: Comprehensive study of mixed eccentricity fault diagnosis in induction motors using signature analysis. Electr. Power Energy Syst. 35, 180–185 (2012)

    Article  Google Scholar 

  32. Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S.: An online Condition monitoring system for induction motors via instantaneous power analysis. J. Mech. Sci. Technol. 29(4), 1483–1492 (2015)

    Article  Google Scholar 

  33. Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S.: An intelligent diagnostic condition monitoring system ac motors via instantaneous power analysis. Int. Rev. Electr. Eng. 8(2), 664–672 (2013)

    Google Scholar 

  34. Bearing failures and their causes, SKF product information 401

  35. Rolling element bearing failures with electrical signature and vibration analysis, All Test Pro., USA, (2009)

  36. Care and maintenance of bearings, Cat.no.3017/E, NTN Corp., Japan

  37. Didier, G., Ternisien, E., Caspary, O., Razik, H.: Fault detection of broken rotor bars in induction motor using a global fault index. IEEE Trans. Ind. Appl. 42(1), 79–88 (2006)

    Article  Google Scholar 

  38. Zhou, W., Habetler, T.G., Harley, R.G.: Incipient bearing fault detection via motor stator current noise cancellation using Wiener filter. IEEE Trans. Ind. Appl. 45(4), 1309–1317 (2009)

    Article  Google Scholar 

  39. Zhou, W., Habetler, T.G., Harley, R.G.: Bearing fault detection via stator current noise cancellation and statistical control. IEEE Trans. Ind. Electron. 55(12), 4260–4469 (2008)

  40. Kia, S.H., Henao, H., Capolino, G.: A high-resolution frequency estimation method for three-phase induction machine fault detection. IEEE Trans. Ind. Electron. 54(4), 2305–2314 (2007)

  41. Bellini, A., Yazidi, A., Filippetti, F., Rossi, C., Capolino, G.A.: High frequency resolution techniques for rotor fault detection of induction machines. IEEE Trans. Ind. Electron. 55(12), 4200–4209 (2008)

    Article  Google Scholar 

  42. Toliyat, H.A., Nandi, S., Choi, S., Kelk, H.S.: Electric Machines, Modelling, Condition Monitoring and Fault Diagnosis. CRC Press, Boca Raton (2012)

  43. Rajagopalan, S., Habetler, T.G., Harley, R.G., Restrepo, J.A., Alle, J.M.: Non-stationary motor fault detection using recent quadratic time-frequency representations. In: International Conference Record of IEEE IAS Annual Meeting, vol. 5, pp. 2333–2339 (2006)

  44. Jung, J.H., Lee, J.J., Kwon, B.H.: Online diagnosis of induction motor using MCSA. IEEE Trans. Ind. Appl. 53(1), 1842–1852 (2006)

  45. Kay, S.M.: Fundamentals of Statistical Signal Processing: Estimation and Detection Theory. Prentice-Hall, Englewood Cliffs (2003)

    Google Scholar 

  46. Vas, P.: Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines. Oxford University Press, New York (1993)

    Google Scholar 

  47. Harmouche, Jinane, Delpha, Claude, Diallo, Demba: Incipient fault detection and diagnosis based on Kullback-Leibler divergence using principal component analysis: part II. Signal Process. 109, 334–344 (2015)

    Article  Google Scholar 

  48. Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., Hung, N.T.: A non-invasive fault diagnosis system for induction motors in noisy environment. In: IEEE Power and Energy Conference, Kuching, Malaysia, 1–3 Dec 2014

  49. Youssef, A., Delpha, C., Diallo, D.: An optimal fault detection threshold for early detection using Kullback-Leibler Divergence for unknown distribution data. Signal Process. 120, 266–279 (2015). doi:10.1016/j.sigpro.2015

    Article  Google Scholar 

  50. Levy, B.C.: Principles of Signal Detection and Parameter Estimation. Springer, New York (2008)

    Book  Google Scholar 

Download references

Acknowledgments

The authors acknowledge the support from the Universiti Teknologi PETRONAS and Ministry of Higher Education (MOHE) Malaysia for the award of the Exploratory Research Grant Scheme (ERGS /1/2012/TK02/UTP/02/09).

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, 31750, Bander Seri Iskander, Perak, Malaysia

    Muhammad Irfan, Nordin Saad, Rosdiazli Ibrahim, Vijanth S. Asirvadam & Muawia Magzoub

Authors
  1. Muhammad Irfan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nordin Saad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rosdiazli Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vijanth S. Asirvadam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Muawia Magzoub
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Irfan.

Appendix

Appendix

The Q-function defined in Eq. (14) [42, 45] is used to measure the error probability of wrong detection.

$$\begin{aligned} Q\left( x \right) =\frac{1}{\sqrt{2\prod }}\int _x^\infty {\exp \left( {-\frac{u^{2}}{2}} \right) } du \end{aligned}$$
(14)

Where:

$$\begin{aligned} x=\frac{\gamma _a -I_{fault} }{\sqrt{{\sigma ^{2}}/N}} \end{aligned}$$

Figure 9 shows the probability distribution curve of the signature amplitude and noise (assuming an additive zero mean Gaussian noise channel). The area under each probability curve is one. There could be two possible errors in decision making of small signal detection. One type of error is miss-detection which is the shaded area on the left side of chosen arbitrary threshold (\(\gamma _a )\). The second type of error is wrong detection which is the shaded area on the right side of chosen arbitrary threshold (\(\gamma _a )\). The reliability of small signal detection mainly depends on how the second type of error (wrong detection) is suppressed.

Fig. 9
figure 9

Probability distribution of fault detection decision errors

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Irfan, M., Saad, N., Ibrahim, R. et al. An Intelligent Fault Diagnosis of Induction Motors in an Arbitrary Noisy Environment. J Nondestruct Eval 35, 12 (2016). https://doi.org/10.1007/s10921-015-0327-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10921-015-0327-3

Keywords

Search

Navigation