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
The article presents a structural diagram and the principle of operation of the installation of a sewing machine for applying a polymer composition to the stitch lines of tarpaulin materials. The calculation schemes a...The article presents a structural diagram and the principle of operation of the installation of a sewing machine for applying a polymer composition to the stitch lines of tarpaulin materials. The calculation schemes and the mathematical model of oscillations of the axis of the composite roller during the application of the polymer composition along the lines of tarpaulin materials are presented. Based on the numerical solution of the problem, the regularities of roller oscillations are presented. The main parameters of the system are substantiated.展开更多
Aiming at the non-stationary feattwes of the roller bearing fault vibration signal, a roller bearing fault diagnosis methtxt based on improved Local Mean Decomposition （LMD） and Support Vector Machine （SVM） is pro...Aiming at the non-stationary feattwes of the roller bearing fault vibration signal, a roller bearing fault diagnosis methtxt based on improved Local Mean Decomposition （LMD） and Support Vector Machine （SVM） is proposed. In this paper, firstly, the wavelet analysis is introduced to the signal decomposition and reconstruction; secondly, the LMD method is used to decompose the recomtnion signal obtained by the wavelet analysis into a ntmaber of Product Ftmctions （PFs） that include main fault characteristics, thus, the initial feattwe vector matrixes could be formed automatically; Thirdly, by applying the Singular Valueition （SVD） techniques to the initial feature vector matrixes, the singular values of the matrixes can be obtained, which can be used as the fault feature vectors of the roller bearing and serve as the input vectors of the SVM classifier; Finally, the recognition results can be obtained from the SVM output. The results of analysis show that the propsed method can be applied to roller beating fault diagnosis effectively.展开更多
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.
文摘The article presents a structural diagram and the principle of operation of the installation of a sewing machine for applying a polymer composition to the stitch lines of tarpaulin materials. The calculation schemes and the mathematical model of oscillations of the axis of the composite roller during the application of the polymer composition along the lines of tarpaulin materials are presented. Based on the numerical solution of the problem, the regularities of roller oscillations are presented. The main parameters of the system are substantiated.
基金supported by Chinese National Science Foundation Grant(No.50775068)China Postdoctoral Science Foundation funded project(No.20080430154)High-Tech Research and Development Program of China(No.2009AA04Z414)
文摘Aiming at the non-stationary feattwes of the roller bearing fault vibration signal, a roller bearing fault diagnosis methtxt based on improved Local Mean Decomposition （LMD） and Support Vector Machine （SVM） is proposed. In this paper, firstly, the wavelet analysis is introduced to the signal decomposition and reconstruction; secondly, the LMD method is used to decompose the recomtnion signal obtained by the wavelet analysis into a ntmaber of Product Ftmctions （PFs） that include main fault characteristics, thus, the initial feattwe vector matrixes could be formed automatically; Thirdly, by applying the Singular Valueition （SVD） techniques to the initial feature vector matrixes, the singular values of the matrixes can be obtained, which can be used as the fault feature vectors of the roller bearing and serve as the input vectors of the SVM classifier; Finally, the recognition results can be obtained from the SVM output. The results of analysis show that the propsed method can be applied to roller beating fault diagnosis effectively.
