Plant-wide monitoring of processes under closed-loop control
Faults in industrial processes produce off-spec products, unsafe conditions, and damage to equipment. According to Misra et al. (1999), just in the U.S. petrochemical industries, an annual loss of $20 billion has been estimated because of poor monitoring and control of such abnormal situations. Therefore, developing efficient methods for on line fault detection and identification has been one of the main tasks in industry. 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). The contributions of this dissertation are summarized as follows:
- 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. Conditions are given so that the minimum corresponds to the true number of PC’s. Ten other methods that are available in the signal processing and chemometrics literature are overviewed and compared with the proposed method. 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. Further, a comparison between the fault identification indexand the contribution plot method is given.
- 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. The multi-block analysis algorithms are basically equivalent to the regular PCA and PLS algorithms. The blocking of process variables in a large scale plant, based on process knowledge, helps to localize the root cause of the fault in a decentralized manner. New definitions of block and variable contributions in the squared prediction error (SPE) and Hotteling’s T2 are proposed for decentralized monitoring. This decentralized monitoring method, based on proper variable blocking, is successfully applied to an industrial polyester film process.
- In Chapter 4 a fault identification approach is proposed using regular PCA. With the multiblock analysis presented in Chapter 5, we propose to integrate the fault identification indexwith MBPCA. First MBPCA is used to identify the block where the fault occurs. Then fault directions are extracted from the fault block only. Instead of using all the sensors to do the identification only the information in the faulty block is used to identify the new fault. Next the original signal is decomposed using wavelets. By keeping the most important wavelet coefficients, we obtain a cleaned and de-noised signal. PCA is applied to this de-noised data to obtain a better fault detection. A MBPCA is also conducted using the de-noised signal, to improve fault identification. The combined multiblock fault identification method is demonstrated on the polyester film process.