LAUSR

Wear caused by contaminated oil or frequent reciprocating of the spool is the chief source of internal leakage in electro-hydraulic directional valves. It is difficult to detect the location and extent of the internal wear of directional valves because the hydraulic transmission works in a closed system. Therefore, this paper focuses on the internal leakage fault diagnosis caused by the wear based on intrinsic mode functions (IMFs) and weighted densely connected convolutional networks (WDenseNets), especially for electro-hydraulic directional valves. First, the empirical mode decomposition is performed in three subsections, respectively, and a series of IMF components are obtained correspondingly. Combined with the ReliefF feature selection algorithm, the optimal IMF component is selected. The weights assigned to the optimal IMF mode are normalized and then weighted as the subsequent diagnostic feature vector. Then, a WDenseNet inspired by squeeze-and-excitation networks is designed. Finally, the weighted optimal IMF component is used as the input for the WDenseNet for fault identification and classification. In addition, an experimental method to investigate the internal leakage faults associated with wear is developed. Theoretical and experimental results indicate that the fault diagnosis method can be used to identify the type of internal leakage and wear location in electro-hydraulic directional valves. Compared with other intelligent diagnosis methods, this method has higher diagnostic accuracy and better training stability. The effectiveness of the proposed method is also verified through an experimental study of the bearing fault in an axial piston pump.