Comparative Study On Fault Diagnosis Methods For CSTR Process

Continuously Stirred Tank Reactor (CSTR) is one of the important equipment in thechemical industry. Many factors, such as the noise, the change of temperature andconcentration in the process of chemical production, make CSTR cannot run normally.The normal operation state of the CSTR process is directly related to the safety andeconomic benefits of the chemical production. So the fault diagnosis of CSTR processis particularly important.According to the working mechanism of CSTR, the corresponding mathematicalmodel is established to simulate the possible faults in the chemical industry and CSTRfault diagnosis system is designed. CSTR simulation experiment platform is used in thethesis.On the basis review of on CSTR process fault diagnosis methods at home and abroad,a fault diagnosis method based on kernel principal component analysis(KPCA) andrecursive least squares support vector machine(RLSSVM) is proposed in this thesis, inorder to enhance the accuracy of fault diagnosis. Firstly, the data are extracted by theKPCA, then the data are classified by the RLSSVM. Compared with classical algorithm,The ensemble method is superior to the traditional method on the accuracy of faultdiagnosis.In order to enhance the speed of fault diagnosis, a fault diagnosis method based onMoving Window KPCA and RLSSVM is proposed in this thesis. The ensemble methodcan save storage space and computing time, while increasing the adaptability of thediagnosis of the model. The experimental results show that the ensemble method caneffectively achieve process monitoring. In terms of diagnostic speed and adaptability,the ensemble method is better than traditional simple methods.