中南大学学报(英文版)
中南大學學報(英文版)
중남대학학보(영문판)
JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY(ENGLISH EDITION)
2010年
6期
1238-1242
,共5页
principal component analysis (PCA)%linear discriminant analysis (LDA)%induction motor%fault diagnosis%fusion algorithm
A feature extraction and fusion algorithm was constructed by combining principal component analysis (PCA) and linear discriminant analysis (LDA) to dctect a fault state of the induction motor. After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment, the reference data were used to produce matching values. In a diagnostic step, two matching values that were obtained by PCA and LDA, respectively, were combined by probability model, and a faulted signal was finally diagnosed. As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief, it shows excellent performance under the noisy environment. The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.
