Industrial Process Monitoring Based On Kernel Partial Least Squares

The modern industrial processes have become more and more large-scale and complex, so the process safety and product quality are two important issues in industries. Also, process monitoring and fault detection have attracted more attention. In the past several years, because of the development of computer technique and the wide utilization of the distributed control system (DCS), large amounts of data have been collected and stored. Therefore, it is necessary and desirable to solve the problems about how to extract useful information from the large amounts of data and how to utilize the obtained information for process safety and product quality control. In this context, data-based multivariate statistical methods have become more popular and have been successfully applied in process modeling, monitoring and control.Traditional multivariate statistical process monitoring (MSPM) methods mainly contain principal component analysis (PCA) and partial least squares (PLS). However, the traditional multivariate statistical-based method has several limitations. One of these limitations is that they require the process variables to be linearly correlated, but nonlinear relationships among different process variables are very common in the process industry, as well as between the process variables and the quality variables. This paper will focus on the nonlinear problem in the process monitoring and employ a nonlinear PLS method, called kernel PLS (KPLS), for online monitoring of industrial processes. The main contributions of this dissertation are summarized as follows:(1) To handle the nonlinear problem for process monitoring, a new technique based on kernel partial least squares (KPLS) is developed. KPLS is to first map the input space into a high-dimensional feature space via a nonlinear kernel function and then to use the standard PLS in that feature space. Compared to linear PLS, KPLS can effectively capture the nonlinear relationship between the input variables and output variables. For process monitoring, two statistics of KPLS method are constructed. KPLS can obtain precise process model and show superior process monitoring performance compared to linear PLS.(2) In order to eliminate the effect of outliers in the modeling data, a robust online monitoring approach is developed and presented for nonlinear process monitoring, which is based on spherical kernel partial least squares (SKPLS). Through projecting the feature vectors onto a unit sphere, we get new feature vectors, and then an ordinary KPLS is applied onto these new feature vectors. Due to the sphering, the influence of outliers is reduced heavily. Compared with ordinary KPLS, the proposed method incorporates a robustness feature for coping with the contaminated modeling data. By integrating a robust method into a kernel based method, the proposed method has both robust and nonlinear characteristic and can be applied to obtain more accurate models for process monitoring purposes.(3) For the batch process, a process monitoring method is developed based on multiway kernel partial least squares (MKPLS). Three-way batch data of normal batch process is unfolded batch-wise, and then kernel partial least squares (KPLS) is applied to capture the nonlinear relationship between the latent structures and predictive variables. Because of its good ability to descript the nonlinear characteristic, MKPLS can detect faults or disturbance more accurately and rapidly than multiway PLS (MPLS), especially for batch process having nonlinear characteristics.(4) An integrated framework consisting of just-in-time-learning (JITL) method and kernel partial least squares (KPLS) is described for the monitoring of the performance of batch processes. The training data set for modeling is defined by using JITL, and KPLS is employed to build the model for process monitoring purpose. The JITL model based on local neighborhoods of similar samples is very accurate and sensitive because it can well track the change of the process. Meanwhile, KPLS is a very efficient technique for tacking complex nonlinear data sets. Furthermore, the proposed monitoring strategy does not require the estimation of future values of the process variables during online application. As a result, the proposed scheme greatly improves the monitoring performance of batch processes.(5) An efficient KPLS-based fault diagnosis framework is proposed to address the problems of the complex kernel matrix calculation and the kernel method based fault identification. A small subset of feature samples is chosen from the large dataset and the KPLS model is built base on the selected feature samples. Since the small subset contains enough information of the process data, the precise model can be obtain and the calculation burden can be reduced significantly. Besides, the proposed fault identification approach can identify the fault source efficiently.Several monitoring methods are compared with the proposed methods, and the TE process and fed-batch penicillin fermentation process are applied to illustrate the efficient of the proposed methods. Finally, some conclusions and future research directions are discussed.