CAJ | 학술논문

针对滚动轴承早期微弱故障特征难以提取的问题，提出基于经验模态分解（Empirical Mode Decomposi-tion，EMD）与最大峭度解卷积（Maximum Kurtosis Deconvolution，MKD）的滚动轴承故障特征提取方法。利用 EMD 方法分解振动信号得到一组固有模态分量（Intrinsic Mode Function，IMF），然后根据时域峭度和包络谱峭度，筛选出敏感 IMF 分量进行信号重构。然后对重构信号进行最大峭度解卷积处理以增强故障信息，最后得到包络功率谱，从而获得轴承故障特征频率信息。通过实验台信号验证了所述方法的有效性及优点。
침대곤동축승조기미약고장특정난이제취적문제，제출기우경험모태분해（Empirical Mode Decomposi-tion，EMD）여최대초도해권적（Maximum Kurtosis Deconvolution，MKD）적곤동축승고장특정제취방법。이용 EMD 방법분해진동신호득도일조고유모태분량（Intrinsic Mode Function，IMF），연후근거시역초도화포락보초도，사선출민감 IMF 분량진행신호중구。연후대중구신호진행최대초도해권적처리이증강고장신식，최후득도포락공솔보，종이획득축승고장특정빈솔신식。통과실험태신호험증료소술방법적유효성급우점。
Aiming at the difficulty in feature extraction of early faults for rolling element bearings,the method based on Empirical Mode Decomposition (EMD)and Maximum Kurtosis Deconvolution (MKD)was proposed to extract features.The vibration signal was decomposed into a group of Intrinsic Mode Functions (IMF)through EMD.According to the kurtosises of time-domain signal and of envelope spectrum,the sensitive IMF components were selected and reconstructed into a new signal.The reconstructed signal was processed by using MKD to enhance the fault information. Finally,the envelope power spectrum was obtained to analyze the bearing fault characteristic frequency information.The effectiveness and advantages of the proposed method were proved by processing the signals collected from test rig.