Hydraulic system fault diagnosis decoupling method based on 2D time-series modeling and self-attention fusion.

ABSTRACT: Hydraulic systems play a pivotal and extensive role in mechanics and energy. However, the performance of intelligent fault diagnosis models for multiple components is often hindered by the complexity, variability, strong hermeticity, intricate structures, and fault concealment in real-world conditions. This study proposes a new approach for hydraulic fault diagnosis that leverages 2D temporal modeling and attention mechanisms for decoupling compound faults and extracting features from multisample rate sensor data. Initially, to address the issue of oversampling in some high-frequency sensors within the dataset, variable frequency data sampling is employed during the data preprocessing stage to resample redundant data. Subsequently, two-dimensional convolution simultaneously captures both t

SUBMITTER: Wang H 

PROVIDER: S-EPMC11228015 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature