Variable-weighted FDA combined with t-SNE and multiple extreme learning machines for visual industrial process monitoring

Abstract

The visualization of an operating state of industrial processes allows operators to identify and diagnose faults intuitively and quickly. The identification and diagnosis of faults are important for ensuring industrial production safety. A method that combines variable-weighted Fisher discriminant analysis (VWFDA), t-distributed stochastic neighbor embedding (t-SNE), and multiple extreme learning machines (ELMs) is proposed for visual process monitoring. First, the VWFDA weighs variables on the basis of their contribution to the fault, thereby amplifying the fault information. The VWFDA is used to extract feature vectors from industrial data, and normal state and various fault states can be separated from each other in the space formed by these feature vectors. Second, t-SNE is used to visualize these feature vectors. Third, given that t-SNE lacks a transformation matrix during dimension reduction, one ELM is used for each class data of t-SNE to obtain the mapping relation from its input data to its mapping points. Finally, the VWFDA and multiple trained ELMs are combined for online process monitoring. The performance of the proposed approach is compared with that of FDA–t-SNE and other methods on the basis of the Tennessee Eastman process, thereby confirming that the proposed approach is advantageous for visual industrial process monitoring.