Gear Fault Diagnosis Based on Empirical Mode Decomposition and 1.5 Dimension SpectrumReport as inadecuate

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Shock and Vibration - Volume 2016 2016, Article ID 5915762, 10 pages -

Research ArticleDepartment of Physics and Electronics, Hunan University of Arts and Science, Changde 415000, China

Received 20 July 2015; Revised 8 November 2015; Accepted 15 November 2015

Academic Editor: Didier Rémond

Copyright © 2016 Jianhua Cai and Xiaoqin Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Aiming at the nonlinear and nonstationary feature of mechanical fault vibration signal, a new fault diagnosis method, which is based on a combination of empirical mode decomposition EMD and 1.5 dimension spectrum, is proposed. Firstly, the vibration signal is decomposed by EMD and the correlation coefficient between each intrinsic mode function and original signal is calculated. Then these intrinsic mode function components, which have a big correlation coefficient, are selected to estimate its 1.5 dimension spectrum. And this method uses 1.5 dimension spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums are summed to obtain the primary power spectrum of gear fault signal. Finally, the information feature of fault is extracted from the reconstructed 1.5 dimension spectrum. A model to reconstruct 1.5 dimension spectrum is established, and the principle and steps of the method are presented. Some simulated and measured gear fault signals have been processed to demonstrate the effectiveness of new method. The result shows that this method can greatly inhibit the interference of Gauss noise to raise the SNR and recognize the secondary phase coupling feature of the signal. The proposed method has a good real-time performance and provides an effective method to determine the early crack fault of gear root.

Author: Jianhua Cai and Xiaoqin Li



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