In view of weak defect signals and large acoustic emission(AE) data in low speed bearing condition monitoring, we propose a bearing fault diagnosis technique based on a combination of empirical mode decomposition(EMD)...In view of weak defect signals and large acoustic emission(AE) data in low speed bearing condition monitoring, we propose a bearing fault diagnosis technique based on a combination of empirical mode decomposition(EMD), clear iterative interval threshold(CIIT) and the kernel-based fuzzy c-means(KFCM) eigenvalue extraction. In this technique, we use EMD-CIIT and EMD to complete the noise removal and to extract the intrinsic mode functions(IMFs). Then we select the first three IMFs and calculate their histogram entropies as the main fault features. These features are used for bearing fault classification using KFCM technique. The result shows that the combined EMD-CIIT and KFCM algorithm can accurately identify various bearing faults based on AE signals acquired from a low speed bearing test rig.展开更多
This paper presents investigations into the influences of bearing clearances on the diagnostic features of monitoring rolling-bearings. A nonlinear dynamic model of a deep groove ball bearing with five degrees of free...This paper presents investigations into the influences of bearing clearances on the diagnostic features of monitoring rolling-bearings. A nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed for numerical analysis under increased radial clearances which are due to not only the scenarios of bearing grades but also gradual wear with bearing service lifetime. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics under different fault cases along with clearance changes. Numerical results show that the vibrations at fault characteristic frequencies exhibit clear inconsistency with common understandings for different cases of increased clearances. This study highlights that it has to take into account the clearance effect, especially for the inner race fault, in order to avoid the under-estimate of fault sizes which may be indicated by the feature amplitude reduction.展开更多
基金the Privileged Shandong Provincial Government’s “Taishan Scholar” Program
文摘In view of weak defect signals and large acoustic emission(AE) data in low speed bearing condition monitoring, we propose a bearing fault diagnosis technique based on a combination of empirical mode decomposition(EMD), clear iterative interval threshold(CIIT) and the kernel-based fuzzy c-means(KFCM) eigenvalue extraction. In this technique, we use EMD-CIIT and EMD to complete the noise removal and to extract the intrinsic mode functions(IMFs). Then we select the first three IMFs and calculate their histogram entropies as the main fault features. These features are used for bearing fault classification using KFCM technique. The result shows that the combined EMD-CIIT and KFCM algorithm can accurately identify various bearing faults based on AE signals acquired from a low speed bearing test rig.
文摘This paper presents investigations into the influences of bearing clearances on the diagnostic features of monitoring rolling-bearings. A nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed for numerical analysis under increased radial clearances which are due to not only the scenarios of bearing grades but also gradual wear with bearing service lifetime. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics under different fault cases along with clearance changes. Numerical results show that the vibrations at fault characteristic frequencies exhibit clear inconsistency with common understandings for different cases of increased clearances. This study highlights that it has to take into account the clearance effect, especially for the inner race fault, in order to avoid the under-estimate of fault sizes which may be indicated by the feature amplitude reduction.
