@article{ART001611709},
author={황철희 and 강명수 and 김종면},
title={A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2011},
volume={16},
number={12},
pages={187-196}
TY - JOUR
AU - 황철희
AU - 강명수
AU - 김종면
TI - A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance
JO - Journal of The Korea Society of Computer and Information
PY - 2011
VL - 16
IS - 12
PB - The Korean Society Of Computer And Information
SP - 187
EP - 196
SN - 1598-849X
AB - Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.
KW - fault detection and classification system;partial autocorrelation coefficient;log spectrum powers;cepstrum coefficients mean;mel-frequency cepstrum coefficient;neural network
DO -
UR -
ER -
황철희, 강명수 and 김종면. (2011). A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance. Journal of The Korea Society of Computer and Information, 16(12), 187-196.
황철희, 강명수 and 김종면. 2011, "A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance", Journal of The Korea Society of Computer and Information, vol.16, no.12 pp.187-196.
황철희, 강명수, 김종면 "A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance" Journal of The Korea Society of Computer and Information 16.12 pp.187-196 (2011) : 187.
황철희, 강명수, 김종면. A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance. 2011; 16(12), 187-196.
황철희, 강명수 and 김종면. "A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance" Journal of The Korea Society of Computer and Information 16, no.12 (2011) : 187-196.
황철희; 강명수; 김종면. A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance. Journal of The Korea Society of Computer and Information, 16(12), 187-196.
황철희; 강명수; 김종면. A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance. Journal of The Korea Society of Computer and Information. 2011; 16(12) 187-196.
황철희, 강명수, 김종면. A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance. 2011; 16(12), 187-196.
황철희, 강명수 and 김종면. "A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance" Journal of The Korea Society of Computer and Information 16, no.12 (2011) : 187-196.
