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...

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Bibliographic Details
Published inJournal of mechanical science and technology Vol. 33; no. 2; pp. 487 - 494
Main Authors Han, Dongying, Zhao, Na, Shi, Peiming
Format Journal Article
LanguageEnglish
Published Seoul Korean Society of Mechanical Engineers 01.02.2019
Springer Nature B.V
대한기계학회
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
Fractal box dimension
Different load
Gear fault diagnosis
PSO-SVM
EMD
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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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1738-494X
1976-3824
DOI:10.1007/s12206-019-0101-z