In order to explore the possible diffusion distance of carbon during proeutectoid ferrite transformation, a slow cooling test of low carbon steel was carried out under vacuum of the thermal simulator. The microstructu...In order to explore the possible diffusion distance of carbon during proeutectoid ferrite transformation, a slow cooling test of low carbon steel was carried out under vacuum of the thermal simulator. The microstructure and thermal expansion curve were discussed and the carbon concentration inside the sample was measured. The ferrite layer of about 450 μm thickness was obtained without pearlite on the surface of the sample in the microstructure. The thermal expansion curve shows that the ferrite layer without pearlite is formed during the local phase transformation, which is followed by the global transformation. The carbon concentration in the core of the sample (0.061%) is significantly higher than that of the bulk material (0.054%). All results show that carbon has long-range diffusion from the outer layer to the inner layer of the sample. The transformation is predominantly interface-controlled mode during local transformation, and the interface migration rate is about 2.25 μm/s.展开更多
A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,wher...A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.展开更多
A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a cla...A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.展开更多
基金Project(16PJ1430200)supported by Shanghai Pujiang Program,China
文摘In order to explore the possible diffusion distance of carbon during proeutectoid ferrite transformation, a slow cooling test of low carbon steel was carried out under vacuum of the thermal simulator. The microstructure and thermal expansion curve were discussed and the carbon concentration inside the sample was measured. The ferrite layer of about 450 μm thickness was obtained without pearlite on the surface of the sample in the microstructure. The thermal expansion curve shows that the ferrite layer without pearlite is formed during the local phase transformation, which is followed by the global transformation. The carbon concentration in the core of the sample (0.061%) is significantly higher than that of the bulk material (0.054%). All results show that carbon has long-range diffusion from the outer layer to the inner layer of the sample. The transformation is predominantly interface-controlled mode during local transformation, and the interface migration rate is about 2.25 μm/s.
基金Projects(61273163,61325015,61304121)supported by the National Natural Science Foundation of China
文摘A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of ChinaProject supported by the Fundamental Research Funds for the Central Universities,China
文摘A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.
