Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors

 

D. Camarena-Martinez¹, R. Osornio-Rios², R. J. Romero-Troncoso³ and A. Garcia-Perez*⁴

 

^1,2,3 HSPdigital-CA Mecatrónica, Facultad de Ingeniería Universidad Autónoma de Querétaro San Juan del Río, Querétaro., México.

³ HSPdigital-CA Telemática, DICIS.

⁴ HSPdigital-CA Procesamiento Digital de Señales, DICIS Universidad de Guanajuato, Salamanca, Guanajuato, México. *agarcia@hspdigital.org

 

ABSTRACT

Detection of failures in induction motors is one of the most important concerns in industry. An unexpected fault in the induction motors can cause a loss of financial resources and waste of time that most companies cannot afford. The contribution of this paper is a fusion of the Empirical Mode Decomposition (EMD) and Multiple Signal Classification (MUSIC) methodologies for detection of multiple combined faults which provides an accurate and effective strategy for the motor condition diagnosis.

Keywords: Empirical mode decomposition, high-resolution spectral analysis, induction motors, multiple-fault diagnosis.

 

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Acknowledgments

This work was supported in part by the National Council on Science and Technology (cONACYT), Mexico, under Scholarship: 229594, SEP PIFI-2014 Universidad de Guanajuato grant, and UAQ-FOFI 2012 projects.

 

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