PSD引导的自适应频带划分方法及其在轴承故障诊断中的应用  

Adaptive Frequency Band Division Method Guided by PSD and Its Application in Bearing Fault Diagnosis

作　　者：汪煜坤 易彩 汪浩[1] 周秋阳 冉乐 王靖元 WANG Yukun;YI Cai;WANG Hao;ZHOU Qiuyang;RAN Le;WANG Jingyuan(State Key Laboratory of Rail Transit Vehicle System,Southwest Jiaotong University,Chengdu 610031)

机构地区：[1]西南交通大学轨道交通运载系统全国重点实验室,成都610031

出　　处：《机械工程学报》2024年第17期179-193,共15页Journal of Mechanical Engineering

基　　金：国家自然科学基金(52272355);四川省自然科学基金(2022NSFSC1918,2023YFG0065);中国博士后科学基金(2019M663899XB)资助项目。

摘　　要：虽然频带划分方法在定位故障共振频带方面取得了很大的成就,但仍然存在共振频带定位不准确的问题。针对定位并提取轴承故障所产生的共振频带较难这一问题,提出一种自适应频带划分方法用于提取轴承故障产生的共振频带。该方法的核心是利用信号的功率谱密度(Power spectral density,PSD)与高斯核函数不断的进行迭代卷积,经过卷积后的PSD曲线会变得更加平滑,再以卷积后的局部极小值点作为自适应频谱划分的边界,随后利用平方包络谱谐波干扰比(Squared envelope spectrum harmonic-to-interference ratio,SESHIR)铺设多层次谱平面,以最大的平方包络谱谐波干扰比来确定共振频带,进行包络解调分析以提取故障特征。通过仿真和实验数据分析,证明该方法能够提取出共振频带,识别出轴承中的故障信息,比快速峭度谱(Fast kurtogram,FK),Autogram以及变分模态分解(Variational mode decomposition,VMD)具有更加良好的性能。Although the band delineation method has made great strides in locating the fault resonance bands,there is still the problem of inaccurate resonance band location.To address the problem that it is difficult to locate the resonance bands generated by bearing faults,an adaptive banding method is proposed to extract the resonance bands generated by bearing faults.The core of the method is to use the power spectral density(PSD)of the signal to iteratively convolve with a gaussian kernel function.The convolved PSD will become smoother,and then the local minima after convolution will be used as the boundary basis for adaptive spectrum partitioning,followed by the use of the squared envelope spectral harmonic noise ratio(SESHIR)to lay out the spectral plane,and the maximum envelope spectral harmonic noise ratio to determine the resonant band,and carry out envelope demodulation analysis to extract fault features.Through simulation and experimental data analysis,it is demonstrated that the method can extract resonance bands and identify fault information in bearings,and has better performance than fast kurtogram(FK),Autogram and variational mode decomposition(VMD).

关 键 词：轴承故障诊断 自适应频谱划分 功率谱密度 平方包络谱谐波干扰比 

分 类 号：TH133[机械工程—机械制造及自动化]