Title :
Incipient bearing fault detection for three-phase Brushless DC motor drive using ANFIS
Author :
Abu-Rub, Haitham ; Ahmed, SK Moin ; Iqbal, Atif ; Toliyat, Hamid A. ; Rahimian, Mina M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas A&M Univ. at Qatar, Doha, Qatar
Abstract :
Incipient fault detection of electrical machine is a major task and requires intelligent diagnostic approach. Extensive research has been performed in the field of automation of fault diagnostic schemes. Among several causes of electrical machine failure the most frequent occurring fault is the mechanical bearing failure. Thus, this paper presents diagnostic technique for incipient bearing failure in a three-phase Brushless DC (BLDC) motor drive system. The Adaptive Neuro-Fuzzy Inference System is utilized for the diagnostic purpose. The proposed approach offers accurate estimate of the bearing conditions with minimal effort. The proposed technique is verified using simulation approach. The simulation is done using Matlab/Simulink and the complete model is presented in the paper.
Keywords :
DC motor drives; brushless DC motors; electric machine analysis computing; fault diagnosis; fuzzy reasoning; ANFIS; Matlab-Simulink; adaptive neurofuzzy inference system; bearing conditions; electrical machine; incipient bearing fault detection; intelligent diagnostic approach; mechanical bearing failure; three-phase Brushless DC motor drive system; Brushless DC motors; Friction; Mathematical model; Rotors; Sensors; Stators; Torque; BLDC drive; Incipient fault; Neuro-Fuzzy Inference; bearing fault;
Conference_Titel :
Diagnostics for Electric Machines, Power Electronics & Drives (SDEMPED), 2011 IEEE International Symposium on
Conference_Location :
Bologna
Print_ISBN :
978-1-4244-9301-2
Electronic_ISBN :
978-1-4244-9302-9
DOI :
10.1109/DEMPED.2011.6063688
