A new structure design method of elastic composite cylindrical roller bearing is proposed, in which PTFE is embedded into a hollow cylindrical rolling element, according to the principle of creative combinations and t...A new structure design method of elastic composite cylindrical roller bearing is proposed, in which PTFE is embedded into a hollow cylindrical rolling element, according to the principle of creative combinations and through innovation research on cylindrical roller bearing structure. In order to systematically investigate the inner wall bending stress of the rolling element in elastic composite cylindrical roller bearing, finite element analysis on different elastic composite cylindrical rolling elements was conducted. The results show that, the bending stress of the elastic composite cylindrical rolling increases along with the increase of hollowness with the same filling material. The bending stress of the elastic composite cylindrical rolling element decreases along with the increase of the elasticity modulus of the material under the same physical dimension. Under the same load, on hollow cylindrical rolling element, the maximum bending tensile stress values of the elastic composite cylindrical rolling element after material filling at 0° and 180° are 8.2% and 9.5%, respectively, lower than those of the deep cavity hollow cylindrical rolling element. In addition, the maximum bending-compressive stress value at 90° is decreased by 6.1%.展开更多
Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined compo...Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.展开更多
基金Project(51175168)supported by the National Natural Science Foundation of ChinaProjects(2011GK3148,2012GK3092)supported by Science and Technology Program of Hunan Province,China
文摘A new structure design method of elastic composite cylindrical roller bearing is proposed, in which PTFE is embedded into a hollow cylindrical rolling element, according to the principle of creative combinations and through innovation research on cylindrical roller bearing structure. In order to systematically investigate the inner wall bending stress of the rolling element in elastic composite cylindrical roller bearing, finite element analysis on different elastic composite cylindrical rolling elements was conducted. The results show that, the bending stress of the elastic composite cylindrical rolling increases along with the increase of hollowness with the same filling material. The bending stress of the elastic composite cylindrical rolling element decreases along with the increase of the elasticity modulus of the material under the same physical dimension. Under the same load, on hollow cylindrical rolling element, the maximum bending tensile stress values of the elastic composite cylindrical rolling element after material filling at 0° and 180° are 8.2% and 9.5%, respectively, lower than those of the deep cavity hollow cylindrical rolling element. In addition, the maximum bending-compressive stress value at 90° is decreased by 6.1%.
基金Projects(City U 11201315,T32-101/15-R)supported by the Research Grants Council of the Hong Kong Special Administrative Region,China
文摘Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.
