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Article Dans Une Revue IEEE Transactions on Industrial Electronics Année : 2013

Detection and Diagnosis of Faults in Induction Motor Using an Improved Ant Clustering Technique

Abdenour Soualhi
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Guy Clerc
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Hubert Razik

Résumé

The presence of electrical and mechanical faults in the induction motors can be detected by analysis of the stator current spectrum. However, when an induction motor is fed by a frequency converter, the spectral analysis of stator current signal becomes difficult. For this reason, the monitoring must depend on multiple signatures in order to reduce the effect of harmonic disturbance on the motor-phase current. The aim of this paper is the description of a new approach for fault detection and diagnosis of induction motors using signal-based method. It is based on signal processing and an unsupervised classification technique called the Artificial Ant Clustering. The proposed approach is tested on a squirrel-cage induction motor of 5.5 kW in order to detect broken rotor bars and bearing failure at different load levels. The experimental results prove the efficiency of our approach compared with supervised classification methods in condition monitoring of electrical machines.
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Dates et versions

hal-00803675 , version 1 (22-03-2013)

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Abdenour Soualhi, Guy Clerc, Hubert Razik. Detection and Diagnosis of Faults in Induction Motor Using an Improved Ant Clustering Technique. IEEE Transactions on Industrial Electronics, 2013, 60 (9), pp.4053 - 4062. ⟨10.1109/TIE.2012.2230598⟩. ⟨hal-00803675⟩
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