Abstract
In this paper, we propose a feature extraction method and fusion algorithm which is constructed by PCA and LDA to detect a fault state of the induction motor that is applied over the whole field of a industry. After yielding a feature vector from current signal which is measured by an experiment using PCA and LDA, we use the reference data to produce matching values. In a diagnostic step, two matching values which are respectively obtained by PCA and LDA are combined by probability model, and a faulted signal is 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 is executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and it showed more excellent performance than the case just using conventional PCA or LDA
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Park, W.J., Lee, S.H., Joo, W.K., Song, J.I. (2007). A Mixed Algorithm of PCA and LDA for Fault Diagnosis of Induction Motor. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_97
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DOI: https://doi.org/10.1007/978-3-540-74205-0_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74201-2
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