A Vibration-Based Approach for Stator Winding Fault Diagnosis of Induction Motors: Application of Envelope Analysis

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Published Sep 29, 2014
Chao Jin Agusmian P. Ompusunggu Zongchang Liu Hossein D. Ardakani Fredrik Petré Jay Lee

Abstract

Induction motors are usually considered as one of the key components in various applications. To maintain the availability of induction motors, it calls for a reliable condition monitoring and prognostics strategy. Among the common induction motor faults, stator winding faults are usually diagnosed with current and voltage signals. However, if the same performance can be achieved, the use of vibration signal is favorable because the winding fault diagnostic method can be integrated with bearing fault diagnostic method which has been successfully proven with vibration signal. Existing work concerning vibration for winding faults often takes it either as auxiliary to magnetic flux, or is not able to detect the winding faults unless severity is already quite significant. This paper proposes a winding fault diagnostic method based on vibration signals measured on the mechanical structure of an induction motor. In order to identify the signature of faults, time synchronous averaging was firstly applied on the raw vibration signals to remove discrete frequency components originating from the dynamics of the shaft and/or gears, and the spectral kurtosis filtering was subsequently applied on the residual signal to emphasize the impulsiveness. For the purpose of enhancing the residual signal in practice, a demodulation technique was implemented with the help of kurtogram. A series of experiments have been conducted on a three-phase induction motor test bed, where stator inter-turn faults can be easily simulated at different loads, speeds and severity levels. The experimental results show that the proposed method was able to detect inter-turn faults in the induction motor, even when the fault is incipient.

How to Cite

Jin, C. ., P. Ompusunggu, A. ., Liu, Z. ., D. Ardakani , H. ., Petré, F. ., & Lee, J. (2014). A Vibration-Based Approach for Stator Winding Fault Diagnosis of Induction Motors: Application of Envelope Analysis. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2388
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Keywords

fault diagnosis, Envelope Analysis, Induction Motor, stator winding faults

References
Al-Atat, H., Siegel, D. & Lee, J. (2011). A systematic methodology for gearbox health assessment and fault classification. Int J Prognostics Health Manage Soc, vol. 2(1), pp. 16.

Antoni, J. & Randall, R. (2006). The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, vol. 20(2), pp. 308-331.

Bechhoefer, E. & Kingsley, M. (2009). A review of time synchronous average algorithms. Annual conference of the prognostics and health management society

Cardoso, A. (1997). The Park's Vector Approach: a general tool for diagnostics of electrical machines, power electronics and adjustable speed drives. Record of the 1997 IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, Carry-le-Rouet, France (261-269)

Eftekhari, M., Moallem, M., Sadri, S. & Hsieh, M.-F. (2013). Online Detection of Induction Motor's Stator Winding Short-Circuit Faults.

Furfari, F. & Brittain, J. (2002). Charles LeGeyt Fortescue and the method of symmetrical components. Industry Applications Magazine, IEEE, vol. 8(3), pp. 7-9.

Jablonski, A. & Barszcz, T. (2013). Validation of vibration measurements for heavy duty machinery diagnostics. Mechanical Systems and Signal Processing, vol. 38(1), pp. 248-263.

Jabłoński, A., Barszcz, T. & Bielecka, M. (2011). Automatic validation of vibration signals in wind farm distributed monitoring systems. Measurement, vol. 44(10), pp. 1954-1967.

Jover Rodríguez, P. V. & Arkkio, A. (2008). Detection of stator winding fault in induction motor using fuzzy logic. Applied Soft Computing, vol. 8(2), pp. 1112- 1120.

Kliman, G., Premerlani, W., Koegl, R. & Hoeweler, D. (1996). A new approach to on-line turn fault detection in AC motors. Industry Applications Conference, 1996. Thirty-First IAS Annual Meeting, IAS'96., Conference Record of the 1996 IEEE (687-693)

Lamim Filho, P., Pederiva, R. & Brito, J. (2014). Detection of stator winding faults in induction machines using flux and vibration analysis. Mechanical Systems and Signal Processing, vol. 42(1), pp. 377- 387.

Lamim, P., Brito, J. N., Silva, V. A. D. & Pederiva, R. (2013). Detection of Electrical Faults in Induction Motors Using Vibration Analysis. Journal of Quality in Maintenance Engineering, vol. 19(4), pp. 2-2.

Randall, R. B. & Antoni, J. (2011). Rolling element bearing diagnostics—a tutorial. Mechanical Systems and Signal Processing, vol. 25(2), pp. 485-520.

Sin, M. L., Soong, W. L. & Ertugrul, N. (2003). Induction machine on-line condition monitoring and fault diagnosis - a survey. Australasian Universities Power Engineering Conference (1-6), Christchurch, New Zealand.

Sottile, J., Trutt, F. C. & Kohler, J. L. (2000). Experimental investigation of on-line methods for incipient fault detection [in induction motors]. Industry Applications Conference (2682-2687)

Trutt, F. C., Sottile, J. & Kohler, J. L. (2002). Online condition monitoring of induction motors. Industry Applications, IEEE Transactions on, vol. 38(6), pp. 1627-1632.

Ukil, A., Chen, S. & Andenna, A. (2011). Detection of stator short circuit faults in three-phase induction motors using motor current zero crossing instants. Electric Power Systems Research, vol. 81(4), pp. 1036-1044.
Section
Technical Research Papers

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