摘要
针对滚动轴承早期故障特征微弱、在噪声和谐波干扰下难以有效提取的问题,提出了联合双时域(DTD)变换和稀疏编码收缩(SCS)的故障诊断方法。首先对原始信号进行双时域变换,将双时域变换谱的对角序列作为重构信号;然后对重构信号进行稀疏编码收缩,减小噪声与低频杂波的干扰;最后对降噪信号做包络谱分析,提取故障特征频率,判定故障类型,实现故障诊断。对仿真信号、实验信号、工程信号的分析结果表明,该方法可有效提取轴承早期故障信号中的微弱故障特征,准确判断故障类型。
Aiming at the weak early fault characteristics of rolling bearings which were difficult to extract under interference of noise and harmonics,a fault diagnosis method was proposed based on DTD transform and SCS.Firstly,original signals were transformed by DTD transform,and the diagonal series of DTD spectrum was used as reconstructed signals.Secondly,the reconstructed signals were handled by SCS method to suppress the interference of noises and low-frequency harmonics.Finally,denoised signals were analyzed by envelope spectrum to extract fault characteristic frequency,judge fault type,and realize fault diagnosis.Analyzed results of simulation signals,experimental signals,and engineering signals show that the proposed method may effectively extract weak fault characteristics in the early fault signals of rolling bearings and accurately judge the fault type.
作者
万书亭
彭勃
王晓龙
WAN Shuting;PENG Bo;WANG Xiaolong(Department of Mechanical Engineering, North China Electric Power University, Baoding, Hebei, 071003)
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2020年第23期2829-2836,共8页
China Mechanical Engineering
基金
国家自然科学基金资助项目(51777075)
河北省自然科学基金资助项目(E2019502064,E2019502047)
中央高校基本科研业务费专项资金资助项目(2019QN131)。
关键词
滚动轴承
特征提取
微弱故障诊断
双时域变换
稀疏编码收缩
rolling bearing
feature extraction
weak fault diagnosis
double time-domain(DTD)transform
sparse coding shrinkage(SCS)