In this paper, a new nonlinear process modeling method called kernel uncorrelated component analysis (KUCA) is proposed. Several advantages of KUCA are summarized as follows. First, KUCA is superior to traditional data-driven modeling methods such as principal component analysis, independent component analysis, and so on, for it has no constraints on data distribution and data structure. In other word, it can be applied to modeling non-Gaussian and nonlinear process. Second, the proposed KUCA allows us to extract uncorrelated components (UCs) from observations directly without considering the second-order or higher order statistic, which is of low computational load. In addition, KUCA is applied to a numerical example to evaluate its performance of source signal separation. Finally, on the basis of KUCA, the corresponding processes monitoring scheme is developed, and the hot galvanizing pickling waste liquor treatment process is taken to evaluate the monitoring performance of the proposed methods. Experiment results demonstrate the feasibility of the proposed monitoring scheme.

This paper develops a brand new technique to perform safety monitoring using images for a type of complex industrial processes. The new safety monitoring technique is developed with the aid of a modified nonnega- tive matrix factorization algorithm which is called multiple- centroid strictly convex nonnegative matrix factorization (MCSCNMF). Specifically, MCSCNMF is designed deliber- ately in an all-new manner such that it can learn more accu- rate centroids for each group of samples than the existing nonnegative matrix factorization-like algorithms. Moreover, MCSCNMF takes advantage of multiple centroids instead of single centroid to describe the complicated distribution of each type of samples. Both properties give MCSCNMF a powerful clustering performance and make it beneficial for developing high-performance safety monitoring tech- niques than the existing nonnegative matrix factorization- like algorithms. We compare different types of monitoring methods in terms of false negative, false positive, and average approximation error in an experiment on a steel coil painting process. Comparison results demonstrate that MCSCNMF-based monitoring technique can obtain the best experimental results compared with other monitoring meth- ods all the time.

In this study, a novel process monitoring method using image data is presented. The new method is referred to as projective nonnegative-tensor factorization (PNTF). Video monitoring systems are increasingly being deployed in complex industrial processes such as manu-facturing processes, food industries, and pharmaceutical process, for extracting information from images to achieve the purpose of process monitoring. Traditional process-monitoring techniques fail to take full advantage of the rich information from color images due to the complex data characteristics including the high-dimensionality and correlation structures. The proposed PNTF is capable of modeling the high-dimensionality structure of red-green-blue data of color images using a tensor analysis technique. Moreover, in contrast to the traditional data-processing algorithms such as principal component analysis, linear discriminant analysis and so on, the PNTF is more applicable to analyzing image data for its nonnegative constraints on matrix factors. In addition, the PNTF allows us to extract features from both grayscale and color images, making process monitoring using multivariate control charts achiev- able. At last, based on the PNTF, methods for detecting and isolating process faults are developed and evaluated through a case study on a production process of electrically melting magnesia.