基于改进二阶循环平稳解卷积的轴承故障检测方法

doi:10.12068/j.issn.1005-3026.2021.05.010

东北大学学报（自然科学版） ›› 2021, Vol. 42 ›› Issue (5): 673-678.DOI: 10.12068/j.issn.1005-3026.2021.05.010

基于改进二阶循环平稳解卷积的轴承故障检测方法

罗忠^1,2，徐迪^1,2，李雷^1,2，马辉^1,2

(1. 东北大学机械工程与自动化学院，辽宁沈阳110819; 2. 东北大学航空动力装备振动及控制教育部重点实验室，辽宁沈阳110819)

修回日期:2020-10-13 接受日期:2020-10-13 发布日期:2021-05-20
通讯作者: 罗忠
作者简介:罗忠(1978-)，男，内蒙古商都人，东北大学教授，博士生导师.
基金资助:
基金项目；(半空) 基金项目.国家自然科学基金资助项目(11872148，U1908217)；中央高校基本科研业务费专项资金资助项目(N2003012， N2003013，N180703018， N170308028)；装备预研领域基金资助项目(61407200107).

Bearing Fault Detection Based on Improved CYCBD Method

LUO Zhong^1,2， XU Di^1,2， LI Lei^1,2， MA Hui^1,2

1. School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China; 2. Key Laboratory of Vibration and Control of Aero-Propulsion System， Ministry of Education， Northeastern University， Shenyang 110819， China.

Revised:2020-10-13 Accepted:2020-10-13 Published:2021-05-20
Contact: LUO Zhong
About author:-
Supported by:
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摘要/Abstract

摘要： 针对涡轴发动机主轴轴承故障特征难以提取，背景噪声干扰大的问题，提出了改进二阶循环平稳解卷积(PSO-CYCBD)方法，用于提取强噪声背景下的故障特征频率.该方法采用粒子群优化(PSO)算法对二阶循环平稳解卷积(CYCBD)方法中的滤波器长度参数进行寻优.首先，基于故障轴承振动特点建立信号模型，然后用PSO算法对包络谱故障特征比(FFR)进行最大化处理，将得到的最优滤波器长度参数输入到CYCBD方法中，对滤波后的信号进行包络谱分析，提取故障特征频率.最后，将提出的方法应用于实测信号中，与传统包络谱分析相比提高了故障特征提取的效率和准确性，从而验证了该方法的有效性.

关键词: 故障诊断;涡轴发动机主轴轴承;粒子群优化;二阶循环平稳解卷积

Abstract: Aiming at the problem that it is difficult to extract the fault features of the main shaft bearing of turboshaft engines， and that the background noise has a large interference， an improved second-order cyclostationary blind deconvolution (CYCBD) method is proposed to extract the fault feature frequency under strong noise background. In this method， particle swarm optimization (PSO) is used to optimize the filter length parameters for the CYCBD method. Firstly， the signal model is established based on the vibration characteristics of the fault bearing， and then the envelope spectrum fault feature ratio (FFR) is maximized by PSO algorithm. The optimal filter length parameter is input into the CYCBD method， and the envelope spectrum of the filtered signal is analyzed to extract the fault feature frequency. Finally， the proposed method is applied to the measured signal， and the efficiency and accuracy of fault feature extraction are improved compared with the traditional envelope spectrum analysis， which verifies the effectiveness of the method proposed.

Key words: fault diagnosis; spindle bearing of turboshaft engine; particle swarm optimization(PSO); second-order cyclostationarity blind deconvolution(CYCBD)

中图分类号:

V214.1

罗忠，徐迪，李雷，马辉. 基于改进二阶循环平稳解卷积的轴承故障检测方法[J]. 东北大学学报（自然科学版）, 2021, 42(5): 673-678.

LUO Zhong， XU Di， LI Lei， MA Hui. Bearing Fault Detection Based on Improved CYCBD Method[J]. Journal of Northeastern University(Natural Science), 2021, 42(5): 673-678.

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基于改进二阶循环平稳解卷积的轴承故障检测方法

Bearing Fault Detection Based on Improved CYCBD Method

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