The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features,making it difficult to identify the fault characteristic frequency.To remove the ...The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features,making it difficult to identify the fault characteristic frequency.To remove the time-varying behavior caused by speed fluctuation,the phase function of target component is necessary.However,the frequency components induced by different faults interfere with each other.More importantly,the complex sideband clusters around the characteristic frequency further hinder the spectrum interpretation.As such,we propose a demodulation spectrum analysis method for multi-fault bearing detection via chirplet path pursuit.First,the envelope signal is obtained by applying Hilbert transform to the raw signal.Second,the characteristic frequency is extracted via chirplet path pursuit,and the other underlying components are calculated by the characteristic coefficient.Then,the energy factors of all components are determined according to the time-varying behavior of instantaneous frequency.Next,the final demodulated signal is obtained by iteratively applying generalized demodulation with tunable E-factor and then the band pass filter is designed to separate the demodulated component.Finally,the fault pattern can be identified by matching the prominent peaks in the demodulation spectrum with the theoretical characteristic frequencies.The method is validated by simulated and experimental signals.展开更多
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
Due to the complexity of investigating deformation mechanisms in helical rolling(HR) process with traditional analytical method, it is significant to develop a 3D finite element(FE) model of HR process. The key formin...Due to the complexity of investigating deformation mechanisms in helical rolling(HR) process with traditional analytical method, it is significant to develop a 3D finite element(FE) model of HR process. The key forming conditions of cold HR of bearing steel-balls were detailedly described. Then, by taking steel-ball rolling elements of the B7008 C angular contact ball bearing as an example, a completed 3D elastic-plastic FE model of cold HR forming process was established under SIMUFACT software environment. Furthermore, the deformation characteristics in HR process were discovered, including the forming process, evolution and distribution laws of strain, stress and damage based on Lemaitre relative damage model. The results reveal that the central loosening and cavity defects in HR process may have a combined effect of large negative hydrostatic pressure(positive mean stress)caused by multi-dimensional tensile stresses, high level transverse tensile stress, and circular-alternating shear stress in cross section.展开更多
基金Project(2018YJS137)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(51275030)supported by the National Natural Science Foundation of China
文摘The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features,making it difficult to identify the fault characteristic frequency.To remove the time-varying behavior caused by speed fluctuation,the phase function of target component is necessary.However,the frequency components induced by different faults interfere with each other.More importantly,the complex sideband clusters around the characteristic frequency further hinder the spectrum interpretation.As such,we propose a demodulation spectrum analysis method for multi-fault bearing detection via chirplet path pursuit.First,the envelope signal is obtained by applying Hilbert transform to the raw signal.Second,the characteristic frequency is extracted via chirplet path pursuit,and the other underlying components are calculated by the characteristic coefficient.Then,the energy factors of all components are determined according to the time-varying behavior of instantaneous frequency.Next,the final demodulated signal is obtained by iteratively applying generalized demodulation with tunable E-factor and then the band pass filter is designed to separate the demodulated component.Finally,the fault pattern can be identified by matching the prominent peaks in the demodulation spectrum with the theoretical characteristic frequencies.The method is validated by simulated and experimental signals.
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Project(2011CB706605)supported by the National Basic Research Program of ChinaProject(IRT13087)supported by the Innovative Research Team Development Program of Ministry of Education of ChinaProject(2012-86)supported by the Grant from the High-end Talent Leading Program of Hubei Province,China
文摘Due to the complexity of investigating deformation mechanisms in helical rolling(HR) process with traditional analytical method, it is significant to develop a 3D finite element(FE) model of HR process. The key forming conditions of cold HR of bearing steel-balls were detailedly described. Then, by taking steel-ball rolling elements of the B7008 C angular contact ball bearing as an example, a completed 3D elastic-plastic FE model of cold HR forming process was established under SIMUFACT software environment. Furthermore, the deformation characteristics in HR process were discovered, including the forming process, evolution and distribution laws of strain, stress and damage based on Lemaitre relative damage model. The results reveal that the central loosening and cavity defects in HR process may have a combined effect of large negative hydrostatic pressure(positive mean stress)caused by multi-dimensional tensile stresses, high level transverse tensile stress, and circular-alternating shear stress in cross section.
