Abstract
An improved singular value decomposition method of gear fault identification based on Hilbert-Huang transform was proposed to overcome the problem of reconstructing a feature matrix of singular value decomposition. The method includes three steps. First, the instantaneous frequency and amplitude matrices were acquired by Hilbert-Huang transform from faulted gear signals. Second, after the matrices were decomposed by singular value decomposition, the defined distances of singular value vectors and the optimal threshold of the distance for classification were calculated. Third, the fault characteristics of a gearbox were identified and classified by the threshold of the distances. The result demonstrates that the proposed method effectively identifies the gear fault and can realize an automatic gear fault diagnosis.
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This paper was recommended for publication in revised form by Associate Editor Eung-Soo Shin
Zhongyuan Su is currently a teacher in School of Energy & Environment, Southeast University, China. He received his PhD degree from Southeast University, China, in March 2008. His research interests include dynamic signal processing, intelligent fault diagnosis, and structure design.
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Su, Z., Zhang, Y., Jia, M. et al. Gear fault identification and classification of singular value decomposition based on Hilbert-Huang transform. J Mech Sci Technol 25, 267–272 (2011). https://doi.org/10.1007/s12206-010-1117-6
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DOI: https://doi.org/10.1007/s12206-010-1117-6