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  • Research Article
  • Open Access

Detection of Bearing Damage Using Stator Current, and Voltage to Cancel Electrical Noise

EURASIP Journal on Advances in Signal Processing20112011:235236

  • Received: 27 July 2010
  • Accepted: 28 January 2011
  • Published:


This paper investigates the detection of a bearing defect in an asynchronous machine by analysing the electric signals. For this purpose, it is considered that the voltage is imposed and independent of mechanical aspect and that the mechanical defect appears only in the current thanks to the variation of impedance. Wiener filtering is used to extract mechanical information contained in the electrical current; this will then enable the use of statistical indicators such as kurtosis which identify the presence of a defect. Initially, the small fluctuation in electric current around the electric cycle (50 Hz) is reduced in order to reinforce cyclostationarity. Then, a filter between the voltage and current is estimated, using Wiener's technique. Since the voltage is decorrelated of mechanical elements, the residual signal (current − predicted current) contains the mechanical part. This study is corroborated by an envelope analysis of the vibration signal. Experimentation on a faulty outer raceway bearing has shown the excellent performance of the proposed method. This method is easier to implement since the sensors' position does not influence the measure the way it does when using accelerometer sensors. This diagnosis could be embedded into a fed converter. However, it is less sensitive than a direct measure of the defect (accelerometer).


  • Statistical Indicator
  • Vibration Signal
  • Full Article
  • Small Fluctuation
  • Mechanical Part

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Authors’ Affiliations

Université de Lyon, F-42023 Saint Etienne, France
Université de Saint Etienne, Jean Monnet, F-42000 Saint-Etienne, France
LASPI, F-42334 IUT de Roanne, France


© Ali Ibrahim et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.