基于重分配魏格纳时频谱和SVD的故障诊断

Fault Diagnosis Based on Reassigned Wigner-Ville Distribution Spectrogram and SVD

时频谱重分配能有效提高时频谱的时频聚集性,减少干扰项。当振动信号中存在着能量较大噪声时,重分配时频谱会受到噪声干扰影响,降低时频分布的可读性。将重分配魏格纳时频谱(RWVDS)和奇异值分解(SVD)结合形成一种新的机械故障诊断方法。利用重分配算法对魏格纳时频谱进行重分配,提高魏格纳时频谱的时频聚集性,再对重分配时频谱进行SVD降噪,降低了噪声干扰影响,提高其时频分布的可读性。该方法对仿真信号、滚动轴承及齿轮箱故障信号进行了分析,并与其他几种方法作了比较。结果表明,该方法时频聚集性好,抗噪能力强,能有效识别强噪声背景下的机械故障特征。

Reassigned spectrogram can effectively enhance the time-frequency concentration and reduce the misleading interference terms,however,the readability of time-frequency distribution of the reassigned spectrogram is reduced when there exists strong background noise in a signal.So,a new method for mechanical fault diagnosis based on reassigned Wigner-Ville distribution spectrogram(RWVDS) and singular value decomposition(SVD) is proposed.The RWVDS is obtained by using the reassigned algorithm to the Wigner-Ville distribution Spectrogram(WVDS).Then,SVD de-noising is applied to the RWVDS to improve the readability.The experimental analysis in the fault signal of simulation signal,rolling bearing and gear box indicates that the proposed method has excellent time-frequency concentration,good noise restraining ability.It can remove noise and reduce misleading interference terms.Therefore,it is more effective for mechanical fault diagnosis.