摘要
针对重分配小波尺度谱存在着时、频分辨率不能同时达到最佳及当振动信号中存在着能量较大的噪声时会降低其时频分布可读性的缺陷,提出一种基于参数优化和奇异值分解(SVD)提高重分配尺度谱时频分布可读性的方法。首先利用Shan-non熵方法优化重分配尺度谱基函数的时间-带宽积(TBP),克服其时、频分辨率不能同时达到最佳的缺陷,再对重分配尺度谱进行SVD降噪降低噪声干扰影响,提高时频分布的可读性。最后用该方法对仿真信号和滚动轴承故障信号进行了分析,结果表明该方法的时频聚集性更好,抗噪能力更强,能更有效地识别强噪声背景下的机械故障特征。
Aiming at the fact that the time-frequency resolutions of reassigned wavelet scalogram cannot simultaneously attain the best and the readability of its time-frequency representation would also be reduced when strong noise exists in the signal,a novel method for improving the readability of the time-frequency representation of reassigned wavelet scalogram is proposed,which is based on parameter optimization and singular value decomposition（SVD）.The time-bandwidth product（TBP） of the wavelet basis is optimized using Shannon entropy,so the problem that the time-frequency resolution of reassigned wavelet scalogram cannot simultaneously attain the best is solved.Then,SVD de-noising is applied to the reassigned wavelet scalogram to reduce the influence of the noise.The results of experiment and engineering application show that the proposed method has excellent time-frequency concentration and good noise restraining ability,and is more effective for mechanical feature extraction.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2010年第6期1330-1334,共5页
Chinese Journal of Scientific Instrument
基金
国家高技术研究发展计划(863计划)(2009AA04Z411)
国家自然科学基金(50875272)
高等学校博士点基金(20090191110005)
重庆大学"211工程"三期建设研究生开放实验室(S-0916)资助项目
关键词
重分配小波尺度谱
奇异值分解
时频分析
特征提取
reassigned wavelet scalogram
singular value decomposition
time-frequency analysis
feature extraction
