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CLC number: TK428; TP391

On-line Access: 2017-02-10

Received: 2015-10-18

Revision Accepted: 2016-02-17

Crosschecked: 2017-01-20

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Yu Liu


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.2 P.272-286


Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines

Author(s):  Jun-hong Zhang, Yu Liu

Affiliation(s):  State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China; more

Corresponding email(s):   Liuyu2012@tju.edu.cn

Key Words:  Diesel, Fault diagnosis, Complete ensemble intrinsic time-scale decomposition (CEITD), Least square support vector machine (LSSVM), Hybrid differential evolution and particle swarm optimization (HDEPSO)

Jun-hong Zhang, Yu Liu. Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 272-286.

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author="Jun-hong Zhang, Yu Liu",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines
%A Jun-hong Zhang
%A Yu Liu
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 2
%P 272-286
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500337

T1 - Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines
A1 - Jun-hong Zhang
A1 - Yu Liu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 2
SP - 272
EP - 286
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500337

Targeting the mode-mixing problem of intrinsic time-scale decomposition (ITD) and the parameter optimization problem of least-square support vector machine (LSSVM), we propose a novel approach based on complete ensemble intrinsic time-scale decomposition (CEITD) and LSSVM optimized by the hybrid differential evolution and particle swarm optimization (HDEPSO) algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components (PRCs) and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.

The paper presents a data driven approach to the analysis of nonstationary engine vibration signals for fault classification. The approach is developed by combining a number of computing techniques including intrinsic time-scale decomposition (ITD) and the parameters optimization problem of least square support vector machine (LSSVM), differential evolution and particle swarm, optimization (HDEPSO) algorithms, which are used for processing the signals and selecting features, and least square support vector machine(LLSVM) for classification. Especially, the development of the proposed ensemble intrinsic time-scale decomposition looks intersting.




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