Because of various error factors,the detecting errors in the real-time experimental data of the wear depth affect the accuracy of the detecting data.The self-made spherical plain bearing tester was studied,and its tes...Because of various error factors,the detecting errors in the real-time experimental data of the wear depth affect the accuracy of the detecting data.The self-made spherical plain bearing tester was studied,and its testing principle of the wear depth of the spherical plain bearing was introduced.Meanwhile,the error factors affecting the wear-depth detecting precision were analyzed.Then,the comprehensive error model of the wear-depth detecting system of the spherical plain bearing was built by the multi-body system theory(MBS).In addition,the thermal deformation of the wear-depth detecting system caused by varying the environmental temperature was detected.Finally,according to the above experimental parameters,the thermal errors of the related parts of the comprehensive error model were calculated by FEM.The results show that the difference between the simulation value and the experimental value is less than 0.005 mm,and the two values are close.The correctness of the comprehensive error model is verified under the thermal error experimental conditions.展开更多
Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degrad...Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.展开更多
基金Project(2014E00468R)supported by Technological Innovation Fund of Aviation Industry Corporation of China
文摘Because of various error factors,the detecting errors in the real-time experimental data of the wear depth affect the accuracy of the detecting data.The self-made spherical plain bearing tester was studied,and its testing principle of the wear depth of the spherical plain bearing was introduced.Meanwhile,the error factors affecting the wear-depth detecting precision were analyzed.Then,the comprehensive error model of the wear-depth detecting system of the spherical plain bearing was built by the multi-body system theory(MBS).In addition,the thermal deformation of the wear-depth detecting system caused by varying the environmental temperature was detected.Finally,according to the above experimental parameters,the thermal errors of the related parts of the comprehensive error model were calculated by FEM.The results show that the difference between the simulation value and the experimental value is less than 0.005 mm,and the two values are close.The correctness of the comprehensive error model is verified under the thermal error experimental conditions.
基金Projects(51475462,61374138,61370031)supported by the National Natural Science Foundation of China
文摘Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.
