Title :
Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique
Author :
Soualhi, Abdenour ; Clerc, Guy ; Razik, H.
Author_Institution :
Lab. Ampere, Univ. de Lyon, Villeurbanne, France
Abstract :
The presence of electrical and mechanical faults in the induction motors (IMs) can be detected by analysis of the stator current spectrum. However, when an IM 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 IMs 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 IM 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.
Keywords :
artificial intelligence; condition monitoring; failure analysis; fault diagnosis; machine bearings; pattern clustering; power engineering computing; squirrel cage motors; artificial ant clustering; bearing failure; broken rotor bar detection; condition monitoring; electrical fault; electrical machines; fault detection-diagnosis; frequency converter; harmonic disturbance; improved artificial ant clustering technique; induction motor; mechanical fault; motor-phase current; power 5.5 kW; signal processing; signal-based method; squirrel-cage IM; stator current signal spectrum analysis; unsupervised classification technique; Fault detection; Feature extraction; Harmonic analysis; Induction motors; Pattern recognition; Stators; Vectors; Artificial intelligence; fault detection; fault diagnosis; feature extraction; induction motors (IMs); monitoring; motor-current signal analysis; pattern recognition (PR); signal processing; squirrel-cage motors;
Journal_Title :
Industrial Electronics, IEEE Transactions on
DOI :
10.1109/TIE.2012.2230598