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Open AccessArticle

A Rail Fault Diagnosis Method Based on Quartic C2 Hermite Improved Empirical Mode Decomposition Algorithm

by 1,2, 3 and 2,*
1
School of Automation, Nanjing Institute of Technology, Nanjing 211167, China
2
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China
3
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(15), 3300; https://doi.org/10.3390/s19153300
Received: 1 June 2019 / Revised: 23 July 2019 / Accepted: 23 July 2019 / Published: 26 July 2019
For compound fault detection of high-speed rail vibration signals, which presents a difficult problem, an early fault diagnosis method of an improved empirical mode decomposition (EMD) algorithm based on quartic C2 Hermite interpolation is presented. First, the quartic C2 Hermite interpolation improved EMD algorithm is used to decompose the original signal, and the intrinsic mode function (IMF) components are obtained. Second, singular value decomposition for the IMF components is performed to determine the principal components of the signal. Then, the signal is reconstructed and the kurtosis and approximate entropy values are calculated as the eigenvalues of fault diagnosis. Finally, fault diagnosis is executed based on the support vector machine (SVM). This method is applied for the fault diagnosis of high-speed rails, and experimental results show that the method presented in this paper is superior to the traditional EMD algorithm and greatly improves the accuracy of fault diagnosis. View Full-Text
Keywords: empirical mode decomposition (EMD); fault diagnosis; Hermite interpolation; kurtosis; approximate entropy empirical mode decomposition (EMD); fault diagnosis; Hermite interpolation; kurtosis; approximate entropy
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Liu, H.; Qin, C.; Liu, M. A Rail Fault Diagnosis Method Based on Quartic C2 Hermite Improved Empirical Mode Decomposition Algorithm. Sensors 2019, 19, 3300.

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