Mechanics & Industry
Volume 17, Number 3, 2016
|Number of page(s)||10|
|Published online||15 February 2016|
Bearing fault diagnosis using Hilbert-Huang transform (HHT) and support vector machine (SVM)
Laboratoire de Technologie Industrielle et de l’Information
(LTII), Faculté de Technologie, Université de Bejaia, 06000
a Corresponding author: firstname.lastname@example.org
Received: 9 September 2013
Accepted: 8 September 2015
This work presents the application of the Hilbert-Huang transform and its marginal spectrum, for the analysis of the stator current signals for bearing faults diagnosis in asynchronous machines. Firstly, the current signals are decomposed into several intrinsic mode functions (IMFs) using the empirical mode decomposition (EMD). The Hilbert Huang spectrum for each IMF is an energy representation in the time-frequency domain using the instantaneous frequency. The marginal spectrum of each IMF can then be obtained. Secondly, the IMFs that includes dominant fault information are modeled using an autoregressive (AR) model. Finally, the AR model parameters serve as the input fault feature vectors to support vector machine (SVM) classifiers. Experimental studies show that the marginal spectrum of the second IMF can be used for the detection and classification of bearing faults. The proposed approach provides a viable signal processing tool for an online machine health status monitoring.
Key words: Signal processing / bearing faults / Hilbert-Huang transform / empirical mode decomposition / Support vector machine / AR model
© AFM, EDP Sciences 2016
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