Performance Monitoring, Fault Diagnosis And Quality Prediction Based On Statistical Theory

In real industrial processes, effective performance monitoring and quality prediction are the key to ensure safety, enhance product quality and economy benefit. For the complex industrial process, it is difficult to achieve the accurate mathematical model. Even if it could be achieved, the equations which are predigested can only describe part relationships of energy and mass. These limit the application of methods based on rigorous mathematical model. On the other hand, with the rapid development of computer technology, a large amount of process data have been sampled and collected. How to transform these collected data into valuable information, and mine it deep-level to improve the monitoring performance becomes a challenge issue. It is one of the most active research areas in the field of process control.Statistical performance monitoring is a method based on statistical theory for online fault detection and diagnosis via analysis to the collected data. The information extracted from process data could reflect the operating status at any time, reduce the losses caused by faults and enhance product quality.Because of the limitation of technology and cost, many key variables are difficult to measure via sensors in industrial process. With the market competition become more and more furious, it has become an important factor holding back the product quality further improvement. Quality prediction and estimation (soft sensor) technology has been proposed to solve this problem. It can estimate the variables that are difficult to measure directly. The results can be used for quality control and decision support. It lays a foundation for the integrated automation.In view of the characteristics of continuous and batch industry processes, some improvements of the tradition monitoring and prediction methods have been made, and new statistical monitoring algorithms are also proposed in this thesis.The main results and contributions of this dissertation are stated as follows:1. Using the advantage of kernel component analysis (KPCA) for nonlinear monitoring and introducing the similarity analysis for fault diagnosis, a new performance monitoring and fault diagnosis method based on KPCA and pattern matching is proposed. Aiming at the existing problem in traditional PCA similarity analysis, the method is improved. Nonlinear principal component similarities are firstly calculated. Then the integrated similarity index is proposed through endowing with different weights to PCA similarity, KPCA similarity and distance similarity. Fault diagnosis is performed through pattern matching of different faults. Effectiveness of the proposed method is verified through TE process.2. In view of the complex industrial processes, a nonlinear process monitoring method based on KICA is proposed through integrating the merit of KPCA to deal with nonlinear data and ICA to extract the high-dimensional information. The data are firstly mapped into high-dimensional feature subspace. Then the ICA algorithm is performed. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. Application results to the FCCU process indicate that the proposed method can effectively capture nonlinear relationship among variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.3. In view of the characteristics of batch process and using the advantage of kernel theory, a novel batch performance monitoring and fault identification strategy based on kernel fisher discriminant analysis (KFDA) is proposed. The approach only uses present data and overcomes pre-estimating the unknown part of process variable trajectory in multi-way PCA (MPCA). The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. Simulation results on a penicillin fermentation process demonstrate that, in comparison to the MPCA method, the proposed method is more accurate and efficient to detect and diagnose the malfunctions.4. Using the advantage of kernel partial least squares (KPLS) in nonlinear regression, a new quality estimation and prediction method based on KPLS is proposed. The basic idea of the method is to first map data in the original space into high-dimensional feature space via nonlinear kernel and then performs quality estimation and prediction. Application results to a simple example and real data in an industrial oil refinery factory show that the proposed method can effectively capture nonlinear relationship among variables and have better estimation performance than PLS and other linear approaches.5. In view of the gross error caused by sensor faults or process leakage, a novel performance monitoring and quality estimation approach based on fisher discriminant analysis (FDA) and kernel regression is proposed. FDA is first used for quality monitoring.If the process is under normal condition, then kernel regression is further used for quality prediction and estimation. Otherwise, if faults have occurred, contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can effectively detect the happening fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.6. The model of performance monitoring and quality prediction is built and a software package is developed through analysis to the characteristic and techniques of methanol process. They are successfully applied to the monitoring of methanol process in Shanghai Coking and Chemical Corporation (SCCC). What we have done lay the foundations for the performance of integrated automation and advanced control in SCCC.