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

3Corresponding author


Received 27 September 2017; accepted 7 October 2017



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