汽车工程

汽车工程 ›› 2020, Vol. 42 ›› Issue (8): 1139-1144.doi: 10.19562/j.chinasae.qcgc.2020.08.020

• • 上一篇    

基于VMD多尺度散布熵的柴油机故障诊断方法*

乔新勇1, 顾程1, 韩立军2   

  1. 1.陆军装甲兵学院车辆工程系,北京 100072;
    2.武警工程大学乌鲁木齐校区,乌鲁木齐 830049
  • 收稿日期:2019-11-19 出版日期:2020-08-25 发布日期:2020-09-24
  • 通讯作者: 乔新勇,副教授,E-mail:qxyaafe@sina.com。
  • 基金资助:
    *军内科研计划项目(2018ZB58)资助。

Diesel Engine Fault Diagnosis Method Based on VMD and Multi-Scale Dispersion Entropy

Qiao Xinyong1, Gu Cheng1, Han Lijun2   

  1. 1. Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072;
    2. Urumqi Campus of Engineering University of PAP, Urumqi 830049
  • Received:2019-11-19 Online:2020-08-25 Published:2020-09-24

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摘要： 为从非平稳非线性的缸盖振动信号中提取出柴油机故障特征,本文中提出一种基于变分模态分解(VMD)的多尺度散布熵的柴油机失火故障诊断方法。利用VMD对柴油机缸盖振动信号进行分解,选取散布熵最小的模态分量作为分析信号,计算该信号的多尺度散布熵,并选取前6个尺度散布熵作为故障特征向量,输入粒子群优化的支持向量机(PSO-SVM)中进行失火故障分类判断,并与其他4种常见方法进行对比,结果表明,本文中提出的诊断方法能够有效提取故障特征,准确识别故障类型,优于所对比方法。

关键词: 柴油机, 故障识别, 变分模态分解, 多尺度散布熵, PSO-SVM

Abstract: In order to extract the diesel engine fault characteristics from the non-stationary and nonlinear cylinder head vibration signal, a fault diagnosis method for diesel engine misfire based on VMD and multi-scale dispersion entropy is proposed. The vibration signal of diesel cylinder head is decomposed by VMD, and then the modal component with the minimum dispersion entropy is selected as the analysis signal to calculate the multi-scale dispersion entropy (MDE). The first six scales of dispersion entropy as fault eigenvector are input into the support vector machine of particle swarm optimization (PSO-SVM ) for misfire fault classification and judgment, which is compared with other four common methods. The results show that the proposed diagnostic method can effectively extract fault features and accurately identify fault types, which is better than the methods compared.

Key words: diesel engine, fault diagnosis, VMD, MDE, PSO-SVM