摘要
鉴于在复杂工况和强背景噪声环境下,滚动轴承的非线性非平稳信号的特征提取非常困难,导致早期故障难以诊断,提出了一种基于局部特征尺度分解(LCD)和最大相关峭度解卷积(MCKD)的故障特征提取方法.首先,利用LCD对信号进行分解,获得一系列瞬时频率具有物理意义的内禀尺度分量(ISC),选取相关系数较大的ISC分量进行重构;然后,利用MCKD方法对重构信号进行处理,增强冲击信号频率,实现降噪;最后,对经LCD-MCKD处理过的信号进行希尔伯特包络谱分析,验证所提方法的有效性.仿真和实验表明该方法能够有效提取故障特征频率,实现故障诊断.
Feature extraction of nonlinear and non-stationary signals of rolling bearings is very difficult in complex conditions and strong background noise,resulting in early failure difficult to diagnose.An intelligent diagnosis method based on local character-scale decomposition and maximum correlated kurtosis deconvolution was proposed.Firstly,the measured vibration signals were processed with local characteristic-scale decomposition(LCD)and decomposed into a series of intrinsic scale component(ISC).Then,The ISC components with large correlation coefficient were selected for reconstruction.The maximum correlated Kurtosis deconvolution(MCKD)method was utilized to process the reconstructed signals,for highlighting the impact signal frequency to achieve noise reduction.Finally,the Hilbert envelope spectrum analysis was performed on the signal obtained by LCD-MCKD,and verified the validity of the proposed method.Simulation and experiments show that this method can effectively extract the fault characteristic frequency and be applied in fault diagnosis.
作者
宿磊
黄海润
李可
苏文胜
SU Lei;HUANG Hairun;LI Ke;SU Wensheng(Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology,Jiangnan University,Wuxi214122,Jiangsu China;State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University ofScience and Technology,Wuhan430074,China;Jiangsu Province Special Equipment Safety Supervision InspectionInstitute Branch of Wuxi,Wuxi214071,Jiangsu China)
出处
《华中科技大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2019年第9期19-24,共6页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(51705203,51775243)
江苏省自然科学基金资助项目(BK20160183,BE2017002)
数字制造装备与技术国家重点实验室开放基金资助项目(DMETKF2018022)
关键词
故障诊断
特征提取
局部特征尺度分解
最大相关峭度解卷积
包络谱
fault diagnosis
feature extraction
local characteristic-scale decomposition(LCD)
maximum correlated kurtosis deconvolution(MCKD)
envelope spectrum