Induction motors broken rotor bars detection using MCSA and neural network: experimental research

  • S. Guedidi
  • S. E. Zouzou
  • W. Laala
  • K. Yahia
  • M. Sahraoui
Original Article

Abstract

Early detection and diagnosis of incipient faults are desirable to ensure an improved operational effectiveness of induction motors. A novel practical method of detection and classification for broken rotor bars, using motor current signature analysis associated with a neural network technique is developed. The motor-slip is calculated via a new simple and very rigorous formula, based on (f s  − f r ) mixed eccentricity harmonic. It can be seen from the experimental study, carried out on hundreds of observation, that the mixed eccentricity harmonic (f s  − f r ) has the largest amplitude in its existence range, under different motor loads and conditions (healthy or defective). Since (f s  − f r ) is related to the slip and the mechanical rotational frequency, it is obvious that the detection of the broken rotor bars harmonics (1 ± 2ks)f s becomes easy. The amplitude of these harmonics and the slip value (detection and discernment criterion) are used as the neural network inputs. The neural network provides a reliable decision on the machine condition. The experimental results obtained from 1.1 and 3 kW motors prove the effectiveness of the proposed method.

Keywords

Induction motor Broken rotor bars Diagnosis MCSA Neural network 

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Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2013

Authors and Affiliations

  • S. Guedidi
    • 1
  • S. E. Zouzou
    • 1
  • W. Laala
    • 1
  • K. Yahia
    • 1
  • M. Sahraoui
    • 1
  1. 1.Laboratoire de Génie Electrique (LGEB), Département de Génie ElectriqueUniversité de BiskraBiskraAlgeria

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