An adversarial-based domain generalization method for the health evaluation of axial piston pumps

Abstract

Abstract The axial piston pump is the power component in hydraulic systems and evaluating its health status is of great importance to the safe operation of hydraulic systems. Discharge pressure signals are common monitoring signals for axial piston pumps, but it is difficult to obtain satisfactory health evaluation results by directly using raw discharge pressure signals since the degradation information lies in some specific frequency bands. Furthermore, the axial piston pump often operates under different operating conditions and most existing condition monitoring methods have low accuracies due to the problems of insufficient degradation data and different data distributions. This paper proposes an adversarial-based domain generalization (DG) method by integrating time-frequency analysis and data augmentation, to accurately predict the health status of axial piston pumps under unknown working conditions. First, discharge pressure signals under various operating conditions are decomposed by using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and effective intrinsic mode functions (IMFs) are selected to train multiple convolutional neural network (CNN) models. Second, a novel data augmentation method based on the modified discrete cosine transform-composite spectrum (DCS) algorithm is introduced to fuse the IMFs from different domains and generate pseudo data sets. Finally, the adversarial training is adopted between the real data and the pseudo data to capture domain-generalized features. The discharge pressure signals of an actual axial piston pump at different health levels were collected on a test bench to verify the effectiveness of the proposed method. Results indicate that the proposed method has a higher prediction accuracy than the comparative methods.