Data-driven tools,such as principal component analysis(PCA)and independent component analysis (ICA)have been applied to different benchmarks as process monitoring methods.The difference between the two methods is that...Data-driven tools,such as principal component analysis(PCA)and independent component analysis (ICA)have been applied to different benchmarks as process monitoring methods.The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latent variables are independent.Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution.However,this kind of constraint cannot be satisfied by several practical processes.To ex- tend the use of PCA,a nonparametric method is added to PCA to overcome the difficulty,and kernel density esti- mation(KDE)is rather a good choice.Though ICA is based on non-Gaussian distribution information,KDE can help in the close monitoring of the data.Methods,such as PCA,ICA,PCA with KDE(KPCA),and ICA with KDE (KICA),are demonstrated and compared by applying them to a practical industrial Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.展开更多
Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators an...Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators and sensors. However, despite of the hardware redundancy, sensor faults have often affected the wind turbine normal operation and thus caused energy generation loss. In this paper, aiming at the wind turbine hydraulic pitch system, data-driven design of process monitoring(PM) and diagnosis has been realized in the wind turbine benchmark. Fault tolerant control(FTC) strategies focused on sensor faults have also been presented here, where with the implementation of soft sensor the sensor fault can be handled and the performance of the system is improved. The performance of this method is demonstrated with the wind turbine benchmark provided by Math Works.展开更多
基金Supported by the National Natural Science Foundation of China （No.60574047） and the Doctorate Foundation of the State Education Ministry of China （No.20050335018）.
文摘Data-driven tools,such as principal component analysis(PCA)and independent component analysis (ICA)have been applied to different benchmarks as process monitoring methods.The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latent variables are independent.Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution.However,this kind of constraint cannot be satisfied by several practical processes.To ex- tend the use of PCA,a nonparametric method is added to PCA to overcome the difficulty,and kernel density esti- mation(KDE)is rather a good choice.Though ICA is based on non-Gaussian distribution information,KDE can help in the close monitoring of the data.Methods,such as PCA,ICA,PCA with KDE(KPCA),and ICA with KDE (KICA),are demonstrated and compared by applying them to a practical industrial Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.
基金the National Natural Science Foundation of China(No.51205018)the Fundamental Research Funds for the Central Universities of China(No.FRF-TP-14-121A2)the Research Project of State Key Laboratory of Mechanical System and Vibration(No.MSV-2014-09)
文摘Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators and sensors. However, despite of the hardware redundancy, sensor faults have often affected the wind turbine normal operation and thus caused energy generation loss. In this paper, aiming at the wind turbine hydraulic pitch system, data-driven design of process monitoring(PM) and diagnosis has been realized in the wind turbine benchmark. Fault tolerant control(FTC) strategies focused on sensor faults have also been presented here, where with the implementation of soft sensor the sensor fault can be handled and the performance of the system is improved. The performance of this method is demonstrated with the wind turbine benchmark provided by Math Works.
