Improved Multi-Distance ARMF Integrated With LTSA Based Pattern Matching Method and Its Application in Fault Diagnosis

Effective dimensionality reduction (DR) and classification in fault diagnosis remain a significant challenge, primarily due to the increasing scale of industrial processes and the non-linear and high-dimensional features of process data. To address this challenge, we present local tangent space alignment (LTSA) integrated with a multi-distance adaptive order morphological filter (MARMF) fault diagnosis method (LTSA-MARMF). In LTSA-MARMF, LTSA that preserves the local manifold structure using tangent space is first utilized for DR to provide the required feature space data for ARMF. Next, the cosine distance and dynamic time warping distance are introduced into the distance error of ARMF, considering the spatial similarity and dynamic features to improve classification accuracy. Finally, the distance-matching result of the pattern is applied to determine the type of fault. Through simulations, it is evident that LTSA-MARMF can achieve more satisfactory fault diagnosis accuracy than other related methods on the Tennessee-Eastman process (TEP) and the actual Grid-connected PV System (GPVS).