The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but m...The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.展开更多
Injection molding is a complicated production technique for the manufacturing of polymer products. During injection molding, it＇s hard to predict molding quality; the injection molding parameters, such as mold temper...Injection molding is a complicated production technique for the manufacturing of polymer products. During injection molding, it＇s hard to predict molding quality; the injection molding parameters, such as mold temperature, melt temperature, packing pressure and packing time, affect the final properties of product. The cavity pressure is a significant key factor. Residual stress and injection molding weight are significantly affected by the cavity pressure. This study created an approach to predict weight of injection-molded by real-time online cavity pressure monitoring. This study uses a 6-inch with thickness lmm light guide panel and the largest area beneath the pressure curve of time as well as the maximum pressure as its characteristic. The upper and lower limits of the control are set to ＋2 standard deviations, and GUI （Graphical User Interface）-based LabVIEW software is used to perform calculation and analysis of the pressure curve. The results of the experiment show that the online internal cavity pressure monitoring system can effectively monitor the quality of the molded products. In 500 injection molding cycle tests, its error rate was less than 8%, whereas the deviation in mass of the molded products selected through the system＇s filtering process was successfully controlled to be within ±4%.展开更多
基金Supported by the 973 project of China （2013CB733600）, the National Natural Science Foundation （21176073）, the Doctoral Fund of Ministry of Education （20090074110005）, the New Century Excellent Talents in University （NCET-09-0346）, ＂Shu Guang＂ project （09SG29） and the Fundamental Research Funds for the Central Universities.
文摘The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.
文摘Injection molding is a complicated production technique for the manufacturing of polymer products. During injection molding, it＇s hard to predict molding quality; the injection molding parameters, such as mold temperature, melt temperature, packing pressure and packing time, affect the final properties of product. The cavity pressure is a significant key factor. Residual stress and injection molding weight are significantly affected by the cavity pressure. This study created an approach to predict weight of injection-molded by real-time online cavity pressure monitoring. This study uses a 6-inch with thickness lmm light guide panel and the largest area beneath the pressure curve of time as well as the maximum pressure as its characteristic. The upper and lower limits of the control are set to ＋2 standard deviations, and GUI （Graphical User Interface）-based LabVIEW software is used to perform calculation and analysis of the pressure curve. The results of the experiment show that the online internal cavity pressure monitoring system can effectively monitor the quality of the molded products. In 500 injection molding cycle tests, its error rate was less than 8%, whereas the deviation in mass of the molded products selected through the system＇s filtering process was successfully controlled to be within ±4%.
