Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component...Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component analysis with discriminatory analysis. Principal component analysis and discriminatory analysis are very important theories in multivariate statistical analysis that has developed quickly in the late thirty years. By means of maximization information method, we choose several earthquake prediction factors whose cumulative proportions of total sam-ple variances are beyond 90% from numerous earthquake prediction factors. The paper applies regression analysis and Mahalanobis discrimination to extrapolating synthetic prediction. Furthermore, we use this model to charac-terize and predict earthquakes in North China (30~42N, 108~125E) and better prediction results are obtained.展开更多
For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control mac...For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.展开更多
One of the major tasks of monitoring production well operations is to determine bottom-hole flowing pressure.The overwhelming majority of wells in the Perm Krai are serviced using borehole pumps,which makes it difficu...One of the major tasks of monitoring production well operations is to determine bottom-hole flowing pressure.The overwhelming majority of wells in the Perm Krai are serviced using borehole pumps,which makes it difficult to take direct bottom-hole flowing pressure measurements.The bottomhole filtration pressure(BHFP)in these wells is very often determined by recalculating the parameters measured at the well mouth(annulus pressure,dynamic fluid level depth).The recalculation is done by procedures based on analytically determining the characteristics of the gas-liquid mixture in the wellbore,which is very inconsistent to perform due to the mixture's complex behavior.This article proposes an essentially different approach to BHFP measurements that relies on the mathematical processing of the findings of more than 4000 parallel mouth and deep investigations of the oil production wells of a large oil-production region.As a result,multivariate mathematical models are elaborated that allow reliably determining the BHFP of oil-production wells in operation.展开更多
文摘Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component analysis with discriminatory analysis. Principal component analysis and discriminatory analysis are very important theories in multivariate statistical analysis that has developed quickly in the late thirty years. By means of maximization information method, we choose several earthquake prediction factors whose cumulative proportions of total sam-ple variances are beyond 90% from numerous earthquake prediction factors. The paper applies regression analysis and Mahalanobis discrimination to extrapolating synthetic prediction. Furthermore, we use this model to charac-terize and predict earthquakes in North China (30~42N, 108~125E) and better prediction results are obtained.
基金National Natural Science Foundation of China (70931004)
文摘For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.
文摘One of the major tasks of monitoring production well operations is to determine bottom-hole flowing pressure.The overwhelming majority of wells in the Perm Krai are serviced using borehole pumps,which makes it difficult to take direct bottom-hole flowing pressure measurements.The bottomhole filtration pressure(BHFP)in these wells is very often determined by recalculating the parameters measured at the well mouth(annulus pressure,dynamic fluid level depth).The recalculation is done by procedures based on analytically determining the characteristics of the gas-liquid mixture in the wellbore,which is very inconsistent to perform due to the mixture's complex behavior.This article proposes an essentially different approach to BHFP measurements that relies on the mathematical processing of the findings of more than 4000 parallel mouth and deep investigations of the oil production wells of a large oil-production region.As a result,multivariate mathematical models are elaborated that allow reliably determining the BHFP of oil-production wells in operation.
