NDLI: Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition

Please wait, while we are loading the content...

Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition

Content Provider	SAGE Publishing
Author	Wang, Jingyue Li, Jiangang Wang, Haotian Guo, Lixin
Copyright Year	2020
Abstract	In order to identify the nonlinear nonstationary pitting-wear fault signal of gears in gearbox, a new method of composite fault diagnosis for gearbox is proposed, which combines empirical mode decomposition with improved variational mode decomposition. Aiming at the problem of false modes in the results of empirical mode decomposition when processing signals, the energy method is used to eliminate and update the mode components, and the correlation coefficient method is used to calculate the correlation between the updated mode components and the original signal. The components with strong correlation are selected to form the combined mode components to weaken the noise and improve the signal-to-noise ratio. Aiming at the problem that variational mode decomposition method needs to manually determine the number of mode components k and the penalty factor α during signal decomposition, a combination of envelope spectral entropy and waveform method is proposed to determine the optimal parameter combination. By analyzing the pitting-wear composite fault vibration signals of gears in the gearbox and the normal signals of the gearbox, the effectiveness of the proposed method is verified, and a comparative analysis with the empirical mode decomposition method is performed to highlight the superiority of the proposed method.
Related Links	https://journals.sagepub.com/doi/pdf/10.1177/1461348420908364?download=true
Starting Page	332
Ending Page	346
Page Count	15
ISSN	14613484
Issue Number	1
Volume Number	40
Journal	Journal of Low Frequency Noise, Vibration and Active Control (LFN)
e-ISSN	20484046
DOI	10.1177/1461348420908364
Language	English
Publisher	Sage Publications UK
Publisher Date	2020-02-26
Publisher Place	London
Access Restriction	Open
Rights Holder	© The Author(s) 2020
Subject Keyword	gearbox envelope spectral entropy empirical mode decomposition improved variational mode decomposition nonlinear Composite fault
Content Type	Text
Resource Type	Article
Subject	Building and Construction Acoustics and Ultrasonics Mechanics of Materials Geophysics Mechanical Engineering Civil and Structural Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings

Fault diagnosis of wind turbine gearbox based on wavelet neural network

Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection

Fault detection enhancement in wind turbine planetary gearbox via stationary vibration waveform data

Refined composite multiscale fuzzy entropy based fault diagnosis of diesel engine

Fault Diagnosis for Gearbox Based on Improved Empirical Mode Decomposition

Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

Variational iteration method with matrix Lagrange multiplier for nonlinear oscillators

An effective modification of He’s variational approach to a nonlinear oscillator

Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition

Similar Documents

Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings

Fault diagnosis of wind turbine gearbox based on wavelet neural network

Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection

Fault detection enhancement in wind turbine planetary gearbox via stationary vibration waveform data

Refined composite multiscale fuzzy entropy based fault diagnosis of diesel engine

Fault Diagnosis for Gearbox Based on Improved Empirical Mode Decomposition

Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

Variational iteration method with matrix Lagrange multiplier for nonlinear oscillators

An effective modification of He’s variational approach to a nonlinear oscillator

Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition