This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is intro...This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.展开更多
Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tole...Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tolerant control method based on fuzzy neural networks was presented for nonlinear systems in this paper. The fault parameters were designed to detect the fault, adaptive updating method was introduced to estimate and track fault, and fuzzy neural networks were used to adjust the fault parameters and construct automated fault diagnosis. And the fault compeusation control force, which was given by fault estimation, was used to realize adaptive fault-tolerant control. This framework leaded to a simple structure, an accurate detection, and a high robusmess. The simulation results in induction motor show that it is still able to work well with high dynamic performance and control precision under the condition of motor parameters＇ variation fault and load torque disturbance.展开更多
基金Supported by the Shandong Natural Science Foundation(ZR2013BL008)
文摘This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.
基金Major State Basic Research Development Program,China(No.2005CB221505)Special Scientific Research Foundation for Doctoral Subject of Colleges and Universities in China(No.20050248058)
文摘Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tolerant control method based on fuzzy neural networks was presented for nonlinear systems in this paper. The fault parameters were designed to detect the fault, adaptive updating method was introduced to estimate and track fault, and fuzzy neural networks were used to adjust the fault parameters and construct automated fault diagnosis. And the fault compeusation control force, which was given by fault estimation, was used to realize adaptive fault-tolerant control. This framework leaded to a simple structure, an accurate detection, and a high robusmess. The simulation results in induction motor show that it is still able to work well with high dynamic performance and control precision under the condition of motor parameters＇ variation fault and load torque disturbance.
