| Human won't be satisfied with obtaining knowledge. Similarly, the safety of production procedure and consistency of product quality are always two goals of the process industry. It is only timely and effectively finding, detecting and restoring fault in process that can create conditions for providing products with good performance and consistent quality, which is also the object and motivation of process monitoring. Industrial process monitoring has developed for seventy years from first appearance of quality control diagram by Shewhart, however, the research for multivariate process monitoring is only longer than ten years. Lots of research results are obtained in this field, though which are always based on two assumptions: One is that process variables are subjected to multivariate normal distribution; the other is that samples are subjected to independent and identical distribution (iid). In fact, the process information in real process is complex and the probability distribution of extracted features is indeterminate. Of course, it is often effective to apply conventional multivariate statistical process control (MSPC) to the process whose process variables are subjected (or approximatively subjected) to multivariate normal distribution. For the process with information subjected to nonnormal distribution, a more effective signal processing method (blind source analysis, BSA) is applied to extract features of process. The research results of this dissertation indicate that process monitoring methods based on BSA will improve the monitoring performance of process and enlarge the range of the application.Two primary mathematical tools used in this dissertation are principal component analysis (PCA) and blind signal analysis (BSA), which are both data-driven methods. PCA is not only used as feature extracting method (where process variables are subjected to multivariate normal distribution), but also as a tool for dimension reduction; BSA is used to extract independent features or process blind source signals from process information in information theory sense, which is more effective than PCA in describing the process.The main contributions of this dissertation are as follows:1) The elementary concepts and scope of process monitoring are introduced. Moreover, PCA and BSA with their application in process monitoring are simpledescribed2)Due to the fact that process information isn't always subjected to multivariate normal distribution, a process monitoring method based on PCA with support vector classifier is provided, which improves the monitoring performance.3)Based on the idea that the process information is driven by a few of components as independent as possible, a novel process monitoring method is provided whose effectiveness is verified by the research results.4)In order to reduce the influence of noises an improved conventional process monitoring method is present, which includes as following steps: firstly extract blind source signals from process information, then denoise each blind source signal with wavelet transform, finally build process statistics to monitor process. The research results verify that it can improve the monitoring performance of process.5)Due to the failure of extracting process features by noise, a process monitoring method based on blind source signal separation with denoising information by wavelet transform is provided. The results of process monitoring indicate that this method is more effective than the process monitoring method based on conventional blind source signal separation.6)Due to the complexity of process information, a process monitoring method which applies independent component analysis and principal component analysis to extract nonnormal distributed process features and normal distributed process features is presented, which avoids the assumption that process information is subjected to multivariate normal distribution. The results of process simulation verify the effectiveness of the presented method.7)Lots of...
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