An improved whole model of beam pumping system was built. In the detail, for surface transmission system(STS), a new mathematical model was established considering the influence of some factors on the STS's torsio...An improved whole model of beam pumping system was built. In the detail, for surface transmission system(STS), a new mathematical model was established considering the influence of some factors on the STS's torsional vibration, such as the time variation characteristic of equivalent stiffness of belt and equivalent rotational inertia of crank. For the sucker rod string(SRS), an improved mathematical model was built considering the influence of some parameters on the SRS's longitudinal vibration, such as the nonlinear friction of plunger, hydraulic loss of pump and clearance leakage. The dynamic response and system efficiency of whole system were analyzed. The results show that there is a jumping phenomenon in the amplitude frequency curve, and the system efficiency is sensitive to motor power, pump diameter, stroke number, ratio of gas and oil, and submergence depth. The simulation results have important significance for improving the efficiency of beam pumping system.展开更多
To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to ac...To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.展开更多
基金Projects(50974108,51174175) supported by the National Natural Science Foundation of China
文摘An improved whole model of beam pumping system was built. In the detail, for surface transmission system(STS), a new mathematical model was established considering the influence of some factors on the STS's torsional vibration, such as the time variation characteristic of equivalent stiffness of belt and equivalent rotational inertia of crank. For the sucker rod string(SRS), an improved mathematical model was built considering the influence of some parameters on the SRS's longitudinal vibration, such as the nonlinear friction of plunger, hydraulic loss of pump and clearance leakage. The dynamic response and system efficiency of whole system were analyzed. The results show that there is a jumping phenomenon in the amplitude frequency curve, and the system efficiency is sensitive to motor power, pump diameter, stroke number, ratio of gas and oil, and submergence depth. The simulation results have important significance for improving the efficiency of beam pumping system.
基金Projects(51375222,51175242)supported by the National Natural Science Foundation of China
文摘To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.
