Article citationsMore>>

Morales-Perez, C., Rangel-Magdaleno, J., Peregrina-Barreto, H., Amezquita-Sanchez, J.P. and Valtierra-Rodriguez, M. (2018) Incipient Broken Rotor Bar Detection in Induction Motors Using Vibration Signals and the Orthogonal Matching Pursuit Algorithm. IEEE Transactions on Instrumentation and Measurement, 67, 2058-2068.
https://doi.org/10.1109/TIM.2018.2813820

has been cited by the following article:

• TITLE: An Adaptive EMD Technique for Induction Motor Fault Detection

  AUTHORS: Manzar Mahmud, Wilson Wang

  KEYWORDS: Induction Motors, Fault Detection, Broken Rotor Bars, Current Signal Processing, Empirical Mode Decomposition

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.10 No.4, November 14, 2019

  ABSTRACT: Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.