奇异值分解降噪结合局部特征尺度分解的轴承故障诊断

Bearing Fault Diagnosis based on Singular Value Decomposition Denoising and Local Characteristic-scale Decomposition

局部特征尺度分解(Local characteristic-scale decomposition,LCD)是一种崭新的自适应时频分析方法,在旋转机械故障诊断领域得到了初步的应用。在研究噪声对LCD影响的基础上,提出了一种奇异值分解(Singular value decomposition,SVD)降噪与LCD相结合的轴承故障诊断方法。首先对信号进行相空间重构,然后运用SVD降噪,对降噪信号进行LCD,将得到的内禀尺度分量进行包络谱分析提取故障特征。通过数据仿真与轴承内圈故障数据分析,验证了该方法的有效性。

As a new self- adaptive time- frequency analysis method,the local characteristic- scale decomposition（ LCD） is proposed lately and applied to mechanical fault diagnosis. The influence of noise to LCD is studied and a new fault diagnosis based on singular value decomposition（ SVD） de- noising and local characteristic- scale decomposition is proposed. Firstly,the phase space reconstruction of original vibration signal is carried out,and then,the noise is effectively eliminated by using singular value decomposition. The LCD of the de- noising signal is carried out and some intrinsic scale components（ ISC） are acquired,then the envelope spectrum analysis on the ISCs is carried out and the fault feature is extracted. Through the analysis of the simulation data and the bearing fault data,the effectiveness of this method is verified.