Mechanics & Industry
Volume 17, Number 2, 2016
|Number of page(s)||14|
|Published online||01 February 2016|
Detection of gear faults in variable rotating speed using variational mode decomposition (VMD)
1 Laboratoire de Mécanique de Précision Appliquée (LMPA),
Institut d’Optique et Mécanique de Précision, Université Ferhat Abbas Sétif 1, Algérie
2 Laboratoire de Mécanique, Modélisation et Production (LA2MP), Ecole Nationale d’ingénieurs de Sfax, Tunisie
a Corresponding author:
Accepted: 6 August 2015
The ensemble empirical mode decomposition (EEMD) was largely used in the diagnosis of the rotating machines, this method could detect the defect at an early stage in the case of non variable speed or slightly variable, but when the speed of the machine varies in acceleration or deceleration the use of the EEMD under these conditions shows a limitation with the detection of the impulses, that are influenced by the presence of the mode mixing, and the end effect. To detect the shocks due to the defect where the variation of speed is forced by the working conditions, we propose to use the Variational Mode Decomposition (VMD) which was recently proposed by Konstantin Dragomiretskiy. This method gave promising results in the detection of the defects on machine elements under non stationary conditions imposed by the variation of speed and torque. In this work, first we show by simulated signal the advantage of VMD compared to the EEMD in the detection of impulses in the case of variable speed and load. Then, we analyze vibration signals given by a dynamic modeling of a gear transmission in the case of non stationary load and speed, for healthy gear and two different of localized faults (early and advanced). The modes are extracted using VMD and followed by calculation of spectrogram and statistics values, which give more information about the defect and allow us to detect it at an early stage.
Key words: Vibration analysis / non stationary operation / time-varying frequency / variational mode decomposition (VMD) / ensemble empirical mode decomposition (EEMD) / intrinsic mode functions (IMFs) / gear fault detection / rotating machines
© AFM, EDP Sciences 2016
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