The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can b...The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can be accurately estimated according to the instantaneous fault characteristic frequency(IFCF). However, in an environment with a low signal-to-noise ratio(SNR), e.g., an incipient fault or function at a low speed, the signal contains strong background noise that seriously affects the effectiveness of the aforementioned method. An algorithm of signal preprocessing based on empirical mode decomposition(EMD) and wavelet shrinkage was proposed in this work. Compared with EMD denoising by the cross-correlation coefficient and kurtosis(CCK) criterion, the method of EMD soft-thresholding(ST) denoising can ensure the integrity of the signal, improve the SNR, and highlight fault features. The effectiveness of the algorithm for rolling element bearing IRF estimation by EMD ST denoising and the IFCF was validated by both simulated and experimental bearing vibration signals at a low SNR.展开更多
Time–frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains.The observation data contai...Time–frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains.The observation data contains three types of noise:the harmonics interference at 50 Hz,high-frequency random noise,and low-frequency noise.We use frequency-domain bandstop filtering to remove the harmonics interference noise,segmentation and extension median filtering,and fitting of fixed extremes in empirical mode decomposition to remove the high-frequency and low-frequency noise,respectively;furthermore,we base the selection of median filtering window size on the variance and skewness coefficient of the data.We first remove the harmonics interference at 50 Hz,then the high-frequency noise,and finally the low-frequency noise.We test the proposed methodology by using theory and experiments,and we find that the three types of noises are removed,the phase and amplitude information of the signal are maintained,and high-quality waveforms are obtained in the time domain.展开更多
基金Project(51275030)supported by the National Natural Science Foundation of ChinaProject(2016JBM051)supported by the Fundamental Research Funds for the Central Universities,China
文摘The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can be accurately estimated according to the instantaneous fault characteristic frequency(IFCF). However, in an environment with a low signal-to-noise ratio(SNR), e.g., an incipient fault or function at a low speed, the signal contains strong background noise that seriously affects the effectiveness of the aforementioned method. An algorithm of signal preprocessing based on empirical mode decomposition(EMD) and wavelet shrinkage was proposed in this work. Compared with EMD denoising by the cross-correlation coefficient and kurtosis(CCK) criterion, the method of EMD soft-thresholding(ST) denoising can ensure the integrity of the signal, improve the SNR, and highlight fault features. The effectiveness of the algorithm for rolling element bearing IRF estimation by EMD ST denoising and the IFCF was validated by both simulated and experimental bearing vibration signals at a low SNR.
基金supported by the National Natural Science Foundation of China(No.41574127 and No.41227803)
文摘Time–frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains.The observation data contains three types of noise:the harmonics interference at 50 Hz,high-frequency random noise,and low-frequency noise.We use frequency-domain bandstop filtering to remove the harmonics interference noise,segmentation and extension median filtering,and fitting of fixed extremes in empirical mode decomposition to remove the high-frequency and low-frequency noise,respectively;furthermore,we base the selection of median filtering window size on the variance and skewness coefficient of the data.We first remove the harmonics interference at 50 Hz,then the high-frequency noise,and finally the low-frequency noise.We test the proposed methodology by using theory and experiments,and we find that the three types of noises are removed,the phase and amplitude information of the signal are maintained,and high-quality waveforms are obtained in the time domain.
