Image-Based Process Monitoring using Projective Nonnegative-Tensor Factorization

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.