基于自相关包络和优化MED的齿轮箱滚动轴承故障诊断  

Gearbox Rolling Bearing Fault Diagnosis Based on Autocorrelation Envelope and Adaptive MED

作　　者：李华 于子峰 肖袁 李志勇 刘海涛 李柏松 邵强 古自强 Li Hua;Yu Zifeng;Xiao Yuan;Li Zhiyong;Liu Haitao;Li Baisong;Shao Qiang;Gu Ziqiang(China Oil&Gas Pipeline Network Corporation,Langfang 065000,China;State Key Laboratory of High-end Compressor and System Technology,Beijing University of Chemical Technology,Beijing 100029,China)

机构地区：[1]国家石油天然气管网集团有限公司,河北廊坊065000 [2]北京化工大学高端压缩机及系统技术全国重点实验室,北京100029

出　　处：《机械传动》2024年第12期139-148,共10页Journal of Mechanical Transmission

基　　金：国家重点研发计划“智能传感器”重点专项2022年度定向项目(2022YFB3207600);国家石油天然气管网集团有限公司科学研究与技术开发项目(CLZB202202)。

摘　　要：最小熵解卷积(Minimum Entropy Deconvolution,MED)是近年来较为流行的一种算法,被广泛应用于齿轮箱、轴承等部件的特征提取与故障诊断。但在实际计算过程中,MED逆滤波器的参数设置对提取结果十分敏感。为解决这个问题,提出了一种优化MED的滚动轴承故障特征提取方法。该方法在MED计算过程中,综合考虑了不同逆滤波器长度下特征频率的能量占比,从而确定最佳逆滤波器参数;同时,利用包络信号的自相关性,进一步增强滚动轴承微弱故障特征信号。通过将自相关包络与优化MED方法相结合,开发了一种新型的齿轮箱滚动轴承特征提取与故障诊断方法。仿真和试验验证说明,该方法能够有效增强与轴承故障相关的特征信号,且优化MED方法明显优于传统的MED和其他相关的轴承信号处理方法,特别是由于自相关包络能够显著增强脉冲成分以及具有良好的去噪特性,在实际齿轮箱轴承故障诊断中的效果更为突出。Minimum entropy deconvolution(MED)is a popular algorithm in recent years,extensively applied in feature extraction and fault diagnosis of components such as gearboxes and bearings.However,in the actual computational process,the parameter settings of the MED inverse filter are highly sensitive to the extraction results.To address this issue,an optimized method was firstly proposed for extracting the fault characteristics of rolling bearings using MED.This method takes into account the energy proportion of feature frequencies under different lengths of inverse filters during the MED computation process,thereby determining the optimal parameters for the inverse filter.Additionally,the self-correlation of envelope signals is utilized to further enhance the weak fault characteristic signals of rolling bearings.By integrating self-correlated envelopes with the optimized MED method,a novel method for feature extraction and fault diagnosis of gearbox rolling bearings has been developed.Simulations and tests have verified that this method can effectively enhance the characteristic signals related to bearing faults,and the optimized MED method is significantly superior to the traditional MED and other related bearing signal processing methods.Notably,the self-correlated envelope,due to its ability to significantly enhance impulse components and its excellent denoising characteristics,shows more prominent results in the actual diagnosis of gearbox bearing faults.

关 键 词：齿轮箱 滚动轴承 最小熵解卷积 自相关包络 

分 类 号：TH133.33[机械工程—机械制造及自动化]