Research and application of KICA-AROMF based fault diagnosis

Abstract

With the development of the modern industrial system, data-driven fault diagnosis methods have attracted more and more attention. Fault diagnosis of complex industrial processes based on one-dimensional adaptive rank-order morphological filter (AROMF) may miss key information because of excessive dimension reduction of process data. To solve this problem, a method combining the kernel independent component analysis (KICA) with one-dimensional AROMF is proposed. Firstly, KICA is used for nonlinear feature extraction, getting the template signal and the test signal of each pattern. Then, a fault diagnosis method via multi-dimensional signals classification method based on AROMF is presented in this paper. The advantage of the proposed method was confirmed by the simulation of the Tennessee Eastman process.