摘要
针对滚动轴承在故障早期特征信号微弱、故障特征提取困难以及单通道分析方法信息利用不充分等问题,提出了一种基于稀疏分解与全矢谱相结合的滚动轴承早期微弱故障特征提取方法。首先,在已构造的冗余字典基础上对滚动轴承同源双通道早期故障信号分别进行稀疏分解,得到各自的稀疏信号;然后,将同源双通道稀疏信号进行全矢信息融合;最后,对融合后的信号进行包络解调分析,以提取出故障特征频率。该方法将全矢谱拓展到早期微弱故障诊断领域,并通过实验验证了其在早期微弱故障特征提取方面的有效性。
As early fault occurs in rolling bearings,the fault feature is hard to be extracted because of its weakness.Single channel analysis methods often result in inadequate use of information.Aiming at the problems mentioned above,a feature extraction method for early fault of rolling bearing based on sparse decomposition and full vector spectrum is proposed.Firstly,using sparse decomposition to process the homologous double channel early fault signals of rolling bearing based on the overcomplete dictionary to obtain the sparse signals.Then using the full vector spectrum technology to fuse the sparse signals obtained.Finally,the hilbert envelope demodulation is carried out to extract the fault feature frequency.The method extends full vector spectrum technology to the early and weak fault diagnosis field and validates its effectiveness in the early and weak feature extraction by experiment.
作者
林辉翼
郝伟
郝旺身
董辛旻
LIN Hui-yi;HAO Wei;HAO Wang-shen;DONG Xin-min(Institute of Vibration Engineering,Zhengzhou University,He’nan Zhengzhou 450001,China)
出处
《机械设计与制造》
北大核心
2019年第6期146-149,共4页
Machinery Design & Manufacture
基金
国家重点研发计划项目“国家质量基础(NQI)的共性技术研究与应用”专项游乐园和景区载人设备全生命周期检测监测与完整性评价技术研究(2016YFF0203100)
关键词
稀疏分解
全矢谱
特征提取
信息融合
滚动轴承
故障诊断
Sparse Decomposition
Full Vector Spectrum
Feature Extraction
Information Fusion
Rolling Bearing
Fault Diagnosis
