Faults in industrial processes produce off-spec products, unsafe conditions, and damage to equipment. This dissertation focuses on the development of process monitoring, fault detection and identification methods that are applied to a polyester film process at DuPont.; The monitoring methods developed in this dissertation are based on principal component analysis (PCA). A new method is presented that is based on the variance of the reconstruction error to select the number of principal components (PC's). This method demonstrates a minimum over the number of PC's. Three data sets are used to test the different methods for selecting the number of PC's: two of them are real process data and the other one is a batch reactor simulation.; A new approach is presented in the use of a fault identification index to identify faults based on fault directions. These are extracted from abnormal data using the singular value decomposition (SVD) method. The proposed method is demonstrated on an industrial polyester film process which is characterized by frequent set-point changes and multiple grade changes.; It is shown that both the loadings and scores of consensus principal component analysis (PCA) can be calculated directly from those of regular PCA, and the multi-block partial least squares (PLS) loadings, weights, and scores can be directly calculated from the regular PLS. The orthogonal properties of four multi-block PCA (MBPCA) and multi-block PLS (MBPLS) algorithms are explored. The use of MBPCA and MBPLS for decentralized monitoring and diagnosis is derived in terms of the regular PCA and PLS scores and residuals.; In Chapter 4 a fault identification approach is proposed using regular PCA. With the multi-block analysis presented in Chapter 5, we propose to integrate the fault identification index with MBPCA. A MBPCA is also conducted using the de-noised signal, to improve fault identification. The combined multi-block fault identification method is demonstrated on the polyester film process. (Abstract shortened by UMI.)
展开▼
