In order to overcome the limitation that rock mass instability warnings are caused by a lack of deep consideration of the inherent mechanism of disaster formation, early warning signs of rock mass instability were det...In order to overcome the limitation that rock mass instability warnings are caused by a lack of deep consideration of the inherent mechanism of disaster formation, early warning signs of rock mass instability were detected and multi-field coupling was analyzed. A multi-field coupling model of a damaged rock mass was established. The relationship between microseismic activity parameters and rock mass stability was analyzed, and a multi-parameter early warning index system was established and its solution program was compiled. Based on the D-S data fusion theory,an early warning model of rock mass instability combining multi-field coupling analysis and microseismic monitoring was constructed. Taking an underground mine stope as an object, the multi-field coupling model and its solution program were used to analyze mining response characteristics. The seismic field data were used to verify the accuracy of the multi-field coupling analysis. The early warning model was used to predict the instability of stope rock mass,and the early warning result is consistent with a real-world scenario.展开更多
Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to d...Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.展开更多
基金Project(2017yfc0602901)supported by the National Key R&D Program of China during the Thirteenth Five-Year Plan PeriodProject(2017zzts204)supported by the Fundamental Research Funds for the Central Universities of Central South University,China
文摘In order to overcome the limitation that rock mass instability warnings are caused by a lack of deep consideration of the inherent mechanism of disaster formation, early warning signs of rock mass instability were detected and multi-field coupling was analyzed. A multi-field coupling model of a damaged rock mass was established. The relationship between microseismic activity parameters and rock mass stability was analyzed, and a multi-parameter early warning index system was established and its solution program was compiled. Based on the D-S data fusion theory,an early warning model of rock mass instability combining multi-field coupling analysis and microseismic monitoring was constructed. Taking an underground mine stope as an object, the multi-field coupling model and its solution program were used to analyze mining response characteristics. The seismic field data were used to verify the accuracy of the multi-field coupling analysis. The early warning model was used to predict the instability of stope rock mass,and the early warning result is consistent with a real-world scenario.
文摘Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.
