Label propagation dictionary learning based process monitoring method for industrial process with between-mode similarity

With the development of the industrial cyber-physical systems, a small amount of labeled data and a large amount of unlabeled data are collected from the industrial process. Due to the variation of internal operation conditions and external environment, there is a between-mode similarity between data samples. The scarcity of labeled data and the existence of similarity make it challenging to extract data characteristics. In addition, it creates new challenges to process monitoring. To solve these problems, this study proposes a label propagation dictionary learning method. We first establish the connection between atoms and corresponding profiles and realize the propagation of their labels through graph Laplacian regularization. Then, considering the similarity of samples in the same class, the low-rank constraint is added to sparse coding to strengthen the mutual propagation of labels. Finally, an optimization method is designed to obtain the dictionary and classifier simultaneously. When new data samples arrive, we conduct process monitoring and condition prediction based on the learned dictionary and classifier. Experiments show that the proposed method can achieve satisfactory monitoring performance when compared to several state-of-the-art methods, indicating the superiority of the proposed method.