Extraction of Features of Diesel Engine Faults Under Transient Operations Based on Order Tracking and Variational Mode Decomposition

任刚; 贾继德; 梅检民; 贾翔宇; 韩佳佳; 尉蓝天

doi:10.13949/j.cnki.nrjgc.2018.04.009

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Extraction of Features of Diesel Engine Faults Under Transient Operations Based on Order Tracking and Variational Mode Decomposition

更新时间：2026-04-16

- Extraction of Features of Diesel Engine Faults Under Transient Operations Based on Order Tracking and Variational Mode Decomposition
- Vol. 39, Issue 4, Pages: 54-60(2018)
- 作者机构：
  
  1. 陆军军事交通学院学员五大队,天津,300161
  2. 陆军军事交通学院投送装备保障系,天津,300161
  3. 武汉理工大学汽车学院,武汉,430070
- 作者简介：
- 基金信息：
- DOI：10.13949/j.cnki.nrjgc.2018.04.009
  CLC：
- Published：2018
- 稿件说明：
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任刚，贾继德，梅检民，贾翔宇，韩佳佳，尉蓝天.基于阶比跟踪与VMD的瞬变工况发动机故障特征提取[J].内燃机工程,2018,39(4):54-60.

任刚, 贾继德, 梅检民, et al. Extraction of Features of Diesel Engine Faults Under Transient Operations Based on Order Tracking and Variational Mode Decomposition[J]. 2018, 39(4): 54-60.
任刚，贾继德，梅检民，贾翔宇，韩佳佳，尉蓝天.基于阶比跟踪与VMD的瞬变工况发动机故障特征提取[J].内燃机工程,2018,39(4):54-60. DOI： 10.13949/j.cnki.nrjgc.2018.04.009.

任刚, 贾继德, 梅检民, et al. Extraction of Features of Diesel Engine Faults Under Transient Operations Based on Order Tracking and Variational Mode Decomposition[J]. 2018, 39(4): 54-60. DOI： 10.13949/j.cnki.nrjgc.2018.04.009.

摘要

针对发动机加速过程振动信号的非平稳性和存在强背景噪声的特点，提出阶比跟踪与变分模态分解（VMD）相结合的方法。对于柴油机曲轴轴承故障和汽油机连杆轴承模拟试验振动信号，利用阶比跟踪技术将时域上的非平稳信号转化为角域上的伪平稳信号，利用VMD对重采样信号进行分解，选择包含故障信息的模态分量，计算其阶比、转速、功率谱所构成的三维阶比谱阵，提取故障特征。仿真分析和故障模拟试验验证了该方法的有效性。

Abstract

A method based on the order tracking and variational mode decomposition(VMD) was proposed to solve the problem of non-smoothness of and big background noise in engine vibration signal. For the vibration signals of diesel engine crankshaft bearing fault and gasoline engine connecting rod bearing fault

a non-smooth signal in the time domain was converted into a pseudo smooth signal in the angular domain based on the order tracking. Then the converted signal was decomposed into several components with VMD

and the components that contain fault information were selected. The three dimensional spectral array of order

speed and power spectrum of the selected components was calculated

and the fault feature was extracted. The effectiveness of the proposed method was verified by simulation and experiment.

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