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

张俊红; 刘昱

doi:10.1631/FITEE.1500337

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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

常规文章 | Updated：2022-05-19

- 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
- Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines
- 信息与电子工程前沿（英文） 2017年18卷第2期页码：272-286
- Affiliations：
  
  1State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
  2China Automotive Technology and Research Center, Tianjin 300300, China
- Author bio：
  
  E-mail:Liuyu2012@tju.edu.cn
- Funds：
  
  Project supported by the National High-Tech R&D Program (863) of China (No. 2014AA041501)
- DOI：10.1631/FITEE.1500337
  中图分类号： TK428; TP391
- 收稿：2015-10-18，
  
  录用：2016-2-17，
  
  纸质出版：2017-02
- Accepted：
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张俊红, 刘昱. 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[J]. 信息与电子工程前沿（英文）, 2017,18(2):272-286.

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.
张俊红, 刘昱. 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[J]. 信息与电子工程前沿（英文）, 2017,18(2):272-286. DOI： 10.1631/FITEE.1500337.

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. DOI： 10.1631/FITEE.1500337.

摘要

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。

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.

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