A Vibration-Based Approach for Stator Winding Fault Diagnosis of Induction Motors: Application of Envelope Analysis
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
fault diagnosis, Envelope Analysis, Induction Motor, stator winding faults
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.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.