利用航空发动机信号特征的振动源盲分离算法

杨广振 1; 荆建平 1; 明阳 2; 颜逸飞 1; 陈昌敏 1

doi:10.7652/xjtuxb201706004

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利用航空发动机信号特征的振动源盲分离算法

更新时间：2025-07-09

- 利用航空发动机信号特征的振动源盲分离算法
- Blind Vibration Sources Separation Method Based on Signal Feature of Aircraft Engine
- 西安交通大学学报 2017年51卷第6期页码：20-27
- 作者机构：
  
  上海交通大学机械与动力工程学院,上海,200240
- 作者简介：
- 基金信息：
  
  : 2016-11-10。作者简介: 杨广振(1991—),男,硕士生;荆建平(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(10772114,11672179)
- DOI：10.7652/xjtuxb201706004
  中图分类号： TN911.72
- 网络首发：2017-06-10，
  
  纸质出版：2017
- 稿件说明：
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杨广振 1, 荆建平 1, 明阳 2, 等. 利用航空发动机信号特征的振动源盲分离算法[J]. 西安交通大学学报, 2017,51(6):20-27.

Blind Vibration Sources Separation Method Based on Signal Feature of Aircraft Engine[J]. 2017, 51(6): 20-27.
杨广振 1, 荆建平 1, 明阳 2, 等. 利用航空发动机信号特征的振动源盲分离算法[J]. 西安交通大学学报, 2017,51(6):20-27. DOI： 10.7652/xjtuxb201706004.

Blind Vibration Sources Separation Method Based on Signal Feature of Aircraft Engine[J]. 2017, 51(6): 20-27. DOI： 10.7652/xjtuxb201706004.

摘要

针对航空发动机机匣观测信号为多个振动源的混合信号的问题

提出了一种基于航空发动机振动信号特征的盲分离算法

能够从混合的观测信号中确定振动源的个数以及提取发动机内部各个振动源的振动信息。算法的核心是基于航空发动机等旋转机械转轴故障振动信号的频谱特点

其故障信号的振动谱一般包含基频、谐波成分和次谐波成分

如不对中、碰磨、裂纹等。算法用到的主要工具是连续小波变换和时间同步平稳法

主要步骤为:首先通过连续小波变换将航空发动机不同观测通道上的观测信号分解

根据谱峰值分析确定主要振动源及对应的基频; 然后通过时间同步平稳法

分别从每个观测通道上提取各个振动源的谐波和次谐波成分

从每个观测通道上提取出了源信号; 对于同一源信号

从每个观测通道上都能提取出一个基本映像

最后通过对比每个映像信号的二范数

确定每个源信号的最优估计。通过数值模拟信号和实测航空发动机加速度振动信号对算法进行了验证

结果表明

在对发动机转速有一定先验知识的情况下

所提算法能够估计出发动机内部的主要振动源的个数

并能提取源信号的主要成分。结合旋转机械振动频谱特点等先验知识

说明了所提算法的正确性和实用性。

Abstract

To solve the difficulty that the observed signals on aircraft engine case are generally a mixture of multiple vibration sources

a blind source separation algorithm is proposed based on the characteristics of aircraft engine vibration sources

where the spectral characteristics of the rotating shaft vibration signal of aircraft engine are aimed at. The vibration spectrum of shaft fault signals of rotating machinery usually includes fundamental frequency

harmonic components and sub-harmonic components

corresponding to such as misalignment

rubbing and crack. The observed signals from different channels are decomposed by continuous wavelet transform

the main vibration sources and the corresponding fundamental frequencies are determined according to the spectral peak analysis

and the harmonics and subharmonic components of each vibration source are extracted from each observed signal with time synchronous averaging method

thus an image of each source can be extracted from each observation channel

and several image signals are obtained for the same source. The optimal estimation is determined by comparing the 2-norm of image signals of each source. The proposed algorithm is validated by numerical simulation signal and measured aerodynamic acceleration signal from aircraft engine case. The results show that the algorithm can estimate the number of main vibration sources inside the engine and extract the main components of each source signal in the case of a certain priori knowledge of the engine speed.

关键词

Keywords

references

LI Z, YAN X, TIAN Z, et al. Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis [J]. Measurement, 2013, 46(1): 259-271.

CHENG W, ZHANG Z, LEE S, et al. Investigations of denoising source separation technique and its application to source separation and identification of mechanical vibration signals [J]. Journal of Vibration and Control, 2014, 20(14): 2100-2117.

MAHVASH MOHAMMADI A. Multi-component machine monitoring and fault diagnosis using blind source separation and advanced vibration analysis [D]. Montreal, Canada: École Polytechnique de Montréal, 2011: 178-201.

HONG H, LIANG M. Separation of fault features from a single-channel mechanical signal mixture using wavelet decomposition [J]. Mechanical Systems and Signal Processing, 2007, 21(5): 2025-2040.

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ZHENG H, LI Z, CHEN X. Gear fault diagnosis based on continuous wavelet transform [J]. Mechanical Systems and Signal Processing, 2002, 16(2): 447-457.

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