Gear fault feature extraction and diagnosis method under different load excitation based on EMD, PSO-SVM and fractal box dimension

Aiming at the problem of gear fault feature extraction and fault classification under different load excitation, we present a new fault diagnosis method that combines three methods, including empirical mode decomposition (EMD), particle swarm optimization support vector machine (PSO-SVM) and fractal...

Full description

Saved in:
Bibliographic Details
Published in:Journal of mechanical science and technology 2019, 33(2), , pp.487-494
Main Authors: Han, Dongying, Zhao, Na, Shi, Peiming
Format: Article
Language:English
Subjects:
Classification
Control
Decomposition
Dynamical Systems
Engineering
Excitation
Fault diagnosis
Feature extraction
Fractals
Industrial and Production Engineering
Mechanical Engineering
Parameters
Particle swarm optimization
Support vector machines
Vibration
기계공학
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Aiming at the problem of gear fault feature extraction and fault classification under different load excitation, we present a new fault diagnosis method that combines three methods, including empirical mode decomposition (EMD), particle swarm optimization support vector machine (PSO-SVM) and fractal box dimension. First, the non-stationary original vibration signal of gear fault is decomposed into several intrinsic mode functions (IMF) by EMD method. Then, the time, frequency, energy characteristic parameters and box dimension are calculated separately from the time domain, frequency domain, energy domain and fractal domain. And then the gear fault characteristics under different load excitation are obtained. Finally, the extracted feature parameters are input into the PSO-SVM model for gear fault classification. The experimental results show that the proposed method can effectively identify gear failure types under different load excitation.
ISSN:1738-494X
1976-3824
DOI:10.1007/s12206-019-0101-z