Grey clustering based diagnosis of induction motor faults - Saman, Mehmet; Aydin, Ilhan; Akin, Erhan (My University, 2009)
In this paper, a fault classification method based on grey clustering is proposed for fault detection of induction motors. The amplitudes of rotor frequency related sideband components obtained through fourier transform of one phase stator current are used for broken rotor bar faults. Park's vector components are extracted from three phase motor currents and then new feature is obtained using principal component analysis on park vector components. Obtained features constitute the inputs of grey clustering algorithm. One broken rotor bar, stator faults and stator and multiple faults are diagnosed. ©2009 IEEE.
Asenkron motor arizalarinin gri kümeleme tabanli teşhisi
2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009 -- 9 April 2009 through 11 April 2009 -- Antalya -- 77539
Keywords: Clustering algorithms; Fault detection; Induction motors; Motors; Parks; Signal processing; Stators; Broken rotor bar; Broken rotor bar fault; Fault classification; Grey clustering; Multiple faults; Park's vector; Phase stators; Rotor frequency; Stator fault; Three phase motor; Vector components; Principal component analysis