噪声与振动控制
譟聲與振動控製
조성여진동공제
NOISE AND VIBRATION CONTROL
2014年
5期
145-149
,共5页
振动与波%经验模态分解(EMD)%平滑伪Wigner-Ville分布(SPWVD)%谱熵%谱峭度%最小二乘支持向量机(LS-SVM)
振動與波%經驗模態分解(EMD)%平滑偽Wigner-Ville分佈(SPWVD)%譜熵%譜峭度%最小二乘支持嚮量機(LS-SVM)
진동여파%경험모태분해(EMD)%평활위Wigner-Ville분포(SPWVD)%보적%보초도%최소이승지지향량궤(LS-SVM)
vibration and wave%empirical mode decomposition (EMD)%smoothed pseudo Wigner-Ville distribution (SPWVD)%spectrum entropy%spectral kurtosis%least squares support vector machine (LS-SVM)
提出一种基于经验模态分解(EMD)和平滑伪Wigner-Ville分布(SPWVD)谱熵的滚动轴承故障诊断的方法。EMD方法充分保留信号本身的非线性和非平稳特征，在信号的滤波和去噪中具有较大的优势，SPWVD谱熵用于定量刻画轴承不同状态下振动信号的时频能量分布，将二种算法相结合应用于不同工作状态滚动轴承，并设计最小二乘支持向量机(LS-SVM)智能模型，实现轴承状态和故障类型的自动分类和识别。通过SPWVD谱熵与谱峭度法的对比，验证了SPWVD谱熵的有效性。实验表明此方法能够有效地提取轴承故障的特征信息，提高轴承故障诊断率。
提齣一種基于經驗模態分解(EMD)和平滑偽Wigner-Ville分佈(SPWVD)譜熵的滾動軸承故障診斷的方法。EMD方法充分保留信號本身的非線性和非平穩特徵，在信號的濾波和去譟中具有較大的優勢，SPWVD譜熵用于定量刻畫軸承不同狀態下振動信號的時頻能量分佈，將二種算法相結閤應用于不同工作狀態滾動軸承，併設計最小二乘支持嚮量機(LS-SVM)智能模型，實現軸承狀態和故障類型的自動分類和識彆。通過SPWVD譜熵與譜峭度法的對比，驗證瞭SPWVD譜熵的有效性。實驗錶明此方法能夠有效地提取軸承故障的特徵信息，提高軸承故障診斷率。
제출일충기우경험모태분해(EMD)화평활위Wigner-Ville분포(SPWVD)보적적곤동축승고장진단적방법。EMD방법충분보류신호본신적비선성화비평은특정，재신호적려파화거조중구유교대적우세，SPWVD보적용우정량각화축승불동상태하진동신호적시빈능량분포，장이충산법상결합응용우불동공작상태곤동축승，병설계최소이승지지향량궤(LS-SVM)지능모형，실현축승상태화고장류형적자동분류화식별。통과SPWVD보적여보초도법적대비，험증료SPWVD보적적유효성。실험표명차방법능구유효지제취축승고장적특정신식，제고축승고장진단솔。
A method of fault diagnosis for rolling bearings based on empirical mode decomposition (EMD) and smoothed pseudo Wigner-Ville distribution (SPWVD) spectral entropy is proposed. In this method, the nonlinear and non-stationary characteristics of the signal in the EMD method, which has a great advantage in signal filtering and de-noising, are fully reserved. The SPWVD spectral entropy is used to quantitatively characterize the time-frequency energy distribution of the vibration signals in different states of the bearing. The intelligent model is designed based on the least square support vector machines (LS-SVM). The automatic classification of bearing state and identification of fault type of the bearing are realized. Through the mutual comparison of the SPWVD spectral entropy method and spectral kurtosis method, the effectiveness of the SPWVD spectral entropy is verified. The results show that this method can effectively extract the characteristics of the bearing fault information and improve the rate of bearing fault diagnosis.
