NDLI: A scale invariant algorithm for the automatic diagnosis of rotor bar failures in induction motors

Content Provider	IEEE Xplore Digital Library
Author	Antonino-Daviu, J. Aviyente, S. Strangas, E. Riera-Guasp, M.
Copyright Year	2011
Description	Author affiliation: Instituto de Ingeniería Energética, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022, SPAIN (Antonino-Daviu, J.; Riera-Guasp, M.) \|\| Department of Electrical and Computer Engineering, Michigan State University, East Lansing, 48824, USA (Aviyente, S.; Strangas, E.)
Abstract	Development of reliable algorithms for the automatic diagnosis of broken rotor bars in induction motors (IM) has become the concern of many researchers during these previous decades. Though conventional steady-state current-based diagnosis approaches have behaved well for certain industrial applications, they may be not suitable in cases in which the machine does not operate under ideal stationary conditions (e.g. presence of load torque oscillations, supply unbalances, noises…). Due to this fact, alternative transient-based techniques based on the application of Time-frequency Decomposition (TFD) tools, have been introduced. They have shown satisfactory results, even in cases in which the conventional methodology does not work properly. Nonetheless, necessity of user expertness for the qualitative interpretation of the resulting time-frequency fault-related patterns as well as lack of automation in the diagnosis process makes often difficult their potential implementation in portable condition monitoring devices. A new algorithm for the automatic diagnostic of rotor bar failures is proposed in this paper. It takes as a basis the wavelet signals resulting from the Discrete Wavelet Transform (DWT) of the startup current, which contain basic fault-related features. These signals are further processed to generate 2-D images containing characteristic L-shaped patterns associated with the analyzed fault. Subsequent application of the scale transform allows obtaining scale-invariant feature matrices. Final correlation between these matrices enables to diagnose the condition of the machine. Test results prove the reliability of the algorithm and its generality to automatically diagnose the fault in machines with rather different sizes and load conditions.
Starting Page	496
Ending Page	501
File Size	487796
Page Count	6
File Format	PDF
ISBN	9781424493104
e-ISBN	9781424493128
e-ISBN	9781424493111
DOI	10.1109/ISIE.2011.5984075
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-27
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Discrete wavelet transforms Correlation Bars Transient analysis Rotors Oscillators
Content Type	Text
Resource Type	Article

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