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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

Clicked: 1578

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yu Liu

http://orcid.org/0000-0003-0946-4488

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

10.1631/FITEE.1500337


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(10): 272-286.

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Abstract: 
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.

应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断

概要:针对固有时间尺度分解算法的模态混叠问题和最小二乘支持向量机的参数优化问题,本文提出了一种新的基于完备集合固有时间尺度分解和混合差分进化和粒子群算法优化最小二乘支持向量机的柴油机故障诊断方法。该方法主要包括以下几个步骤:首先,为解决固有时间尺度分解算法的模态混叠问题,提出了一种完备集合固有时间尺度分解算法。随后,利用完备集合固有时间尺度分解算法将非平稳的柴油机振动信号分解为一系列平稳的旋转分量和残差信号。然后,提取了前几阶旋转分量的三类典型的时频特征,包括奇异值、旋转分量能量和能量熵、AR模型参数,作为故障特征。最后,提出了混合差分进化和粒子群算法对最小二乘支持向量机的参数进行优化的方法,并通过将故障特征输入训练好的最小二乘支持向量机模型实现故障诊断。仿真和实验结果表明提出的故障诊断方法可以克服固有时间尺度分解的模态混叠问题,而且能够准确识别柴油机故障。

关键词:柴油机;故障诊断;完备集合固有时间尺度分解;最小二乘支持向量机;混合差分进化和粒子群优化算法

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