The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and t...The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.展开更多
This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-E...This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method,this research has considered the tunnel's geometric,strength,and operational factors as the dependent variables.At first,multiple regression(MR) method was used to propose equations based on various parameters.The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy.As such,adaptive neuro-fuzzy inference system(ANFIS),was used to evaluate its capabilities in terms of predicting surface settlement.Among generated ANFIS models,the model with all input parameters considered produced the best prediction,so as its associated R^2 in the test phase was obtained to be 0.957.The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors.For sensitivity analysis of ANFIS model,cosine amplitude method(CAM) was employed; among other dependent variables,fill factor of grouting(n) and grouting pressure(P) were identified as the most affecting parameters.展开更多
In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using concept...In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry 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(2007AA041401,2007AA04Z194) supported by the National High Technology Research and Development Program of China
文摘The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.
文摘This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method,this research has considered the tunnel's geometric,strength,and operational factors as the dependent variables.At first,multiple regression(MR) method was used to propose equations based on various parameters.The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy.As such,adaptive neuro-fuzzy inference system(ANFIS),was used to evaluate its capabilities in terms of predicting surface settlement.Among generated ANFIS models,the model with all input parameters considered produced the best prediction,so as its associated R^2 in the test phase was obtained to be 0.957.The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors.For sensitivity analysis of ANFIS model,cosine amplitude method(CAM) was employed; among other dependent variables,fill factor of grouting(n) and grouting pressure(P) were identified as the most affecting parameters.
基金Project(08SK1002) supported by the Major Project of Science and Technology Department of Hunan Province,China
文摘In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry 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.
