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Bearing Fault Diagnosis Based on Optimal Time-Frequency Representation Method ?

机译:基于最佳时频表示方法的轴承故障诊断

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Wigner-Ville Distribution (WVD)is probably the most used non-linear time-frequency distribution for signal processing in fault diagnosis, due to the advantages of excellent resolution and localization in time-frequency domain. However, the presence of cross terms when they are applied to multicomponent signals can give misleading interpretations. A methodology based onLocal Mean Decomposition (LMD)andWVDis proposed to get more reliable bearing fault diagnosis based on vibration signals.Kullback-Leibler Divergence (KLD)guides the selection of the optimal frequency band with the most relevant information about the fault. Early results based on experimental data show successful diagnosis.
机译:Wigner-Ville分布(WVD)可能是故障诊断中信号处理最常用的非线性时频分布,这归因于其出色的时频域分辨率和定位优势。但是,将交叉项应用于多分量信号时,它们的存在会产生误导性的解释。提出了一种基于局部均值分解(LMD)和WVDis的方法,以基于振动信号获得更可靠的轴承故障诊断。基于实验数据的早期结果表明诊断成功。

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