A Review Study on Mathematical Methods for Fault Detection Problems in Induction Motors

Öz

—Induction motors are frequently used in industrial processes. Failure of these machines may cause economic, quality and safety losses. In this paper, the mathematical methods used in detection of mechanical and electrical faults of these motors are reviewed together with theory and application examples on the current and vibration data which is acquired during performance tests of the motors followed by accelerated aging

Anahtar Kelimeler

• -

Kaynakça

1. S.Nandi, H.Toliyat, XLi, (2005). Condition monitoring and fault diagnosis of electrical motors – a review. IEEE Transactions on Energy Conversion. 20 (4), pp.719-729.
2. A.K.S.Jardine, D.Lin, D.Banjevic, (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing. (20), pp.1483–1510.
3. A.H.Bonnett, (2000). Root Cause AC Motor Failure Analysis with a Focus on Shaft Failures. IEEE Transactions on Industry Applications. 36 (5), pp.1435-1448.
4. R.R.Schoen, T.G.Habetler, F.Kamran, R.G.Bartheld, (1995). Motor Bearing Damage Detection Using Stator Current Monitoring. IEEE Transactions on Industry Applications. 31 (6), pp.1274-1279.
5. IEEE Standard Test Procedure for Evaluation of Systems of Insulation Materials for Random-wound AC Electric Machinery, IEEE Std 117- 1974.
6. A.S.Erbay, B.R.Upadhyaya, (1999) Multi sensor fusion for induction motor aging analysis and fault diagnosis. Research Report, UTK,UTNE/BRU/99-01. Knoxville, TN, USA.
7. S.Şeker, E.Ayaz, B.R.Upadhyaya, A.S.Erbay, Analysis of Motor Current and Vibration Signals for Detecting Bearing Damage in Electric Motors. Maintenace And Reliability Conference. Knoxville, TN, USA, 8-10 May 2000, (1) pp.29.01-29.14.
8. S.Seker, (2000). Determination of Air-Gap Eccentricity in Electric Motors Using Coherence Analysis. IEEE Power Engineering Review. July, pp.48-50.

9. S.Şeker, E.Ayaz, E.Türkcan, (2003). Elman’s Recurrent Neural Network Applications to Condition Monitoring in Nuclear Power Plant and Rotating Machinery. Engineering Applications of Artificial Intelligence. 16 (7-8), pp.647–656.
10. S.Şeker, E.Ayaz, (2003). A Reliability Model for Induction Motor Ball Bearing Degradation. Electric Power Components & Systems. 31 (7), pp.639-652.
11. S.Şeker, E.Ayaz, (2002). A Study on Condition Monitoring for Induction Motors Under the Accelerated Aging Processes. IEEE Power Engineering Review. 22 (7), pp.35-37.
12. S.Şeker, E.Ayaz, (2003). Feature extraction related to bearing damage in electric motors by wavelet analysis. Journal of the Franklin Institute. 340 (2), pp.125-134.
13. E.Ayaz, S.Şeker, E.Türkcan, B.Barutçu, Combination Of Spectral And Multi-Resolution Wavelet Analysis For Fault Detection In Electric Motors. ELECO’2003, Third International Conference on Electrical and Electronics Engineering. Bursa, Turkey, 3-7 December 2003, pp.94-98.
14. E.Ayaz, A.Öztürk, S.Şeker, B.R.Upadhyaya, (2009). Fault detection based on continuous wavelet transform and sensor fusion in electric motors. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering. 28 (2), pp.454- 470.
15. E.Ayaz, (2014). Autoregressive modeling approach of vibration data for bearing fault diagnosis in electric motors. Journal of Vibroengineering. 16 (5), pp.2130-2138.
16. C.C.Wang, Y.Kang, P.C.Shen, Y.P.Chang, Y.L.Chung, (2010). Applications of fault diagnosis in rotating machinery by using time series analysis with neural network. Expert Systems with Applications. (37), pp.1696–1702.
17. M.S.Yilmaz, E.Ayaz, Adaptive Neuro-Fuzzy Inference System for Bearing Fault Detection in Induction Motors Using Temperature, Current, Vibation Data. EUROCON 2009-International IEEE Conference. Saint-Petersburg, Russia, May 18-23, 2009.
18. N.T.Nguyen, H.H.Lee, Bearing fault diagnosis using adaptive network based fuzzy inference system. International Symposium on Electrical & Electronics Engineering. Vietnam, 24- 25 October 2007.
19. M.S.Ballal, Z.J.Khan, H.M.Suryawanshi, R.L.Sonolikar, (2007). Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor. IEEE Transactions on Industrial Electronics. 54 (1).
20. E.Ayaz, M.Uçar, S.Şeker, B.R.Upadhyaya, (2009). Neuro-detector based on coherence analysis for stator insulation in electric motors. Electric Power Components and Systems. 37 (5), pp.533-546.
21. V.S.Vaseghi, (1996) Advanced signal processing and digital noise reduction, New York: John Wiley.
22. T.K.Moon, W.C.Stirling, (1999) Mathematical Methods and Algorithms for Signal Processing, Prentice Hall.
23. S.Qian, D.Chen, (1996) The joint Time -Frequency Analysis-Methods and Applications. Englewood Cliffs, NJ: Prentice-Hall.
24. I. Daubechies, (1990). The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory 36 (5) pp.961-1005.
25. A.Papoulis, (1987) Probability, Random Variables and Stochastic Processes. McGraw Hill International Edition, 5th printing, Singapore.
26. M.H.Hayes, (1996) Statistical Digital Signal Processing and Modeling. John Wiley&Sons, Inc.
27. L.H.Tsoukalas, R.E.Uhrig, (1997) Fuzzy and neural approaches in engineering. New York: Wiley.
28. J.S.R.Jang, (1993). Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23 (3).