The Research Of PLS And Its External Methods Appplied In Process Monitoring

Production safety and product quality have been two major themes needed to research in process industry.Fault diagnosis technology,developed since 1970s,has been an important branch of Process Monitoring field.Among the fault diagnosis methods, the statistical method based on data is more promising,which is because it is difficult to establish the precise model of the industrial process and the relations between process variates are strong coupling.Partial Least Squares(PLS) analysis method is a data dimensionality reduction technology,which extracts useful information from data to build process model using statistical theory.This thesis studied the basic principal of PLS and its application in Fault Detection and Diagnosis(FDD).By the means of multivariate statistical charts and contribution histogram plots,PLS and Discriminant PLS methods were applied in fault detection and diagnosis of simulated systems.The simulation results were expressed by statistical charts and contribution plots.In order to improve the fault detection ratio under high noise,wavelet partial least squares (WPLS) fault diagnosis method was proposed,and its effectiveness was illustrated by the application in the numerical example. Finally,the superior performance of WPLS for process monitoring was reflected in Tennessee-Eastman Process(TEP) simulation experiment.Comparison of fault diagnosis results among PLS,Discriminant PLS,WPLS showed that,the results of Discriminant PLS increased by around 11 percent,results of WPLS method raised by 34 percent,compared with results of PLS for the average fault detection ratio.Moreover, WPLS method has the capabilities of detecting unknown fault better than Discriminant PLS.Furtherly,WPLS makes great improvment in reducing faulse report and omitted report,and WPLS than PLS,DPLS better able to detect relatively weak industrial process changes,which enhances the accuracy of the fault detection and diagnosis.Therefore,the WPLS fault diagnosis method is a more effective means of process monitoring.