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Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

Journal of Vibroengineering. 2018;20(4):1603-1618 DOI 10.21595/jve.2017.18917

 

Journal Homepage

Journal Title: Journal of Vibroengineering

ISSN: 1392-8716 (Print); 2538-8460 (Online)

Publisher: JVE International

LCC Subject Category: Technology: Mechanical engineering and machinery

Country of publisher: Lithuania

Language of fulltext: English

Full-text formats available: PDF, XML

 

AUTHORS


Rahmoune Chemseddine (Solid Mechanics and Systems Laboratory (LMSS), University M’hamed Bougara Boumerdes, Boumerdes, Algeria)

Merainani Boualem (Solid Mechanics and Systems Laboratory (LMSS), University M’hamed Bougara Boumerdes, Boumerdes, Algeria)

Benazzouz Djamel (Solid Mechanics and Systems Laboratory (LMSS), University M’hamed Bougara Boumerdes, Boumerdes, Algeria)

Fedala Semchedine (Applied Precision Mechanics Laboratory, Institute of Optics and Precision Mechanics, Setif 1 University, Setif, Algeria)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 25 weeks

 

Abstract | Full Text

Vibration signal of gearbox systems carries the important dynamic information for fault diagnosis. However, vibration signals always show non stationary behavior and overwhelmed by a large amount of noise make this task challenging in many cases. Thus, a new fault diagnosis method combining the Hilbert empirical wavelet transform (HEWT), the singular value decomposition (SVD) and Elman neural network is proposed in this paper. Vibration signals of normal gear, gear with tooth root crack, gear with chipped tooth in width, gear with chipped tooth in length, gear with missing tooth and gear with general surface wear are collected in different speed and load conditions. HEWT, a new self-adaptive time-frequency analysis, was applied to the vibration signals to obtain the instantaneous amplitude matrices. Singular value vectors, as the fault feature vectors were then acquired by applying the SVD. Last, the Elman neural network was used for automatic gearbox fault identification and classification. Through experimental results, it was concluded that the proposed method can accurately extract and classify the gear fault features under variable conditions. Moreover, the performance of the proposed HEWT-SVD method has an advantage over that of Hilbert-Huang transform (HHT)-SVD, local mean decomposition (LMD)-SVD or wavelet packet transform (WPT)-PCA for feature extraction.