Title :
Motor Misalignment Detection Based on Hidden Markov Model
Author :
Wongsuwan, Tichate ; Tangamchit, Poj ; Prapanavarat, Cherdchai ; Pusayatanont, Mongkol
Author_Institution :
Dept. of Control Syst. & Instrum. Eng., King Mongkut´´s Inst. of Technol., Bangkok
fDate :
Oct. 18 2006-Sept. 20 2006
Abstract :
This paper purpose a fault detection technique in three-phase induction motors based on hidden Markov model (HMM). This technique detects misalignment of motor´s shaft by using HMM recognition of stator´s current using cepstral coefficients as feature vectors. We divided the amount misalignment into six group levels. In each level, we trained the HMM with a set of data that represents each level of misalignment, the experiments indicated 83.34% recognition rate
Keywords :
fault diagnosis; hidden Markov models; induction motors; cepstral coefficients; fault detection technique; feature vectors; hidden Markov model; motor misalignment detection; three-phase induction motors; Air gaps; Cepstral analysis; Control system synthesis; Fault detection; Frequency; Hidden Markov models; Induction motors; Rotors; Shafts; Stator windings; Feature Vector; Hidden Markov Model; Motor Misalignment; Wavelet Transform;
Conference_Titel :
Communications and Information Technologies, 2006. ISCIT '06. International Symposium on
Conference_Location :
Bangkok
Print_ISBN :
0-7803-9741-X
Electronic_ISBN :
0-7803-9741-X
DOI :
10.1109/ISCIT.2006.339981
