Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis

EURASIP Journal on Image and Video Processing

Table 2 Overall classification accuracy for LBP, CLBP, SIFT, and wavelet-based fault diagnosis schemes

LBP-based scheme		CLBP-based scheme		SIFT- and wavelet-based schemes [6]
Feature extraction operator	Classification accuracy (%)	Feature extraction operator	Classification accuracy (%)	Feature extraction method	Classification accuracy (%)
LBP_8,1	100	CLBP_S _8,1_M _8,1	100	SIFT	97.916
${LBP}_{8, 1}^{u 2}$	97.9167	$CLBP_S_{8, 1}^{u 2}_M_{8, 1}^{u 2}$	98.9583	Wavelet variance	89.582
${LBP}_{8, 1}^{ri}$	100	$CLBP_S_{8, 1}^{ri}_M_{8, 1}^{ri}$	100	Wavelet cross-correlation	79.165
${LBP}_{8, 1}^{riu 2}$	98.9583	$CLBP_S_{8, 1}^{riu 2}_M_{8, 1}^{riu 2}$	100