Gearbox fault diagnosis based on VMD-MSE and adaboost classifier
Dengwei Song1, Chen Lu2, Jian Ma3
School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing, 100191, China
E-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
Abstract. Accurate and efficient fault diagnosis is of great importance for gearbox. This study proposed a fault diagnosis based on variational mode decomposition (VMD) – multiscale entropy (MSE) and adaboost algorithm. First, the VMD is employed to decompose the raw signal in time‑frequency domain. Then, MSE is computed to generate the feature vectors. Finally, the classifier based on adaboost is training and several weak classifiers form a strong classifier to realize the fault diagnosis. The feasibility and accuracy of the method is validated by the data from the Prognostics and Health Management Society for the 2009 data challenge competition.
Keywords: gearbox, fault diagnosis, variational mode decomposition, multiscale entropy, adaboost.
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Cite this article
Song Dengwei, Lu Chen, Ma Jian Gearbox fault diagnosis based on VMD‑MSE and adaboost classifier. Vibroengineering PROCEDIA, Vol. 14, 2017, p. 120‑125.
© JVE International Ltd. Vibroengineering PROCEDIA. Oct 2017, Vol. 14. ISSN 2345-0533