Uncorrelated discriminant graph embedding for fault classification

Abstract

AbstractRecently, graph embedding methods have been successfully used in process monitoring. To improve the discriminant power, a novel supervised graph embedding method, called uncorrelated discriminant graph embedding (UDGE), is proposed. Different from the unsupervised design of locality preserving projection (LPP), UDGE utilizes both the local geometrical structure and label information to construct the similarity between different data points. The “local geometrical structure” means that each data point can be represented as a combination of its neighbours. Due to add the uncorrelated constraint, the extracted features of UDGE are statistically uncorrelated. Uncorrelated attributes are essential for dimension reduction since they contain minimum redundancy. The application of UDGE is evaluated on the Tennessee Eastman process (TEP) benchmark. Experimental results show UDGE can better separate different types of faults and provide more promising fault diagnosis performance. The code of UDGE is released in https://github.com/htz-ecust/Uncorrelated-Discriminant-Graph-Embedding-for-Fault-Classification.