| This research presents several innovations in industrial process modeling and monitoring with the purpose of better controlling the process.; In semiconductor manufacturing industry, people show increased interested in thermal modeling of Low-Pressure Chemical Vapor Deposition (LPCVD) processes in order to understand the process better and get tighter control of film uniformity. In this dissertation, a first principles transformed linear model is developed for the LPCVD process to address drawbacks of existing thermal models and facilitate, its industrial implementation.; In this work, a new valve suction model is proposed with simple structure and straightforward logic which make it easy to implement. Furthermore, several published valve suction detection techniques are reviewed. The inconsistency of Horch's first method is theoretically analyzed and illustrated by a simulated example. A new valve suction detection method is proposed based on curve-fitting for both self-regulating and integrating processes. The new method shows superior performance to other existing methods.; In this work, a new fault diagnosis method using fault directions in Fisher Discriminant Analysis (FDA) is developed in attempt to provide a better solution than the traditional contribution plot based on PCA. Besides, a new process monitoring method is proposed which consists of data pre-analysis, fault visualization and fault diagnosis.; In today's chemical industry, massive amount of data are easily available in computer controlled processes. But at the same time, the visualization of high dimensional data has been difficult. A general framework of hierarchical visualization is proposed and several multivariate visualization methods are developed in this work. The performance of PCA, PLS, Class Preserving Projection (CPP), FDA and two proposed approaches based on Support Vector Machines (SVM) are compared using an industrial data set. (Abstract shortened by UMI.)... |
