Fault detection of axial piston pumps is of great significance to improve the reliability and life of fluid power systems. However, it is difficult to detect multiple faults on key lubricating interfaces due to the liquid-solid coupling. This paper proposes a fault detection strategy of the three key lubricating interfaces based on the one against all (OAA) and spare support vector machine (SSVM). The parameter sparsity is imposed to deal with the performance degradation of OAA-SVM model as a result of the imbalanced dataset. Experimental investigations on the benchmark dataset and axial piston pumps are carried out. Results show that the OAA-SSVM model accuracies of the benchmark dataset and axial piston pump are 96.67% and 95.83%, respectively, which are better than the OAA-SVM model. The recall rates of the bearing fault 3 and pump fault 2 can decrease by 13.33% and 10.00%, respectively. And the false discovery rates of the normal bearing and normal pump can be reduced by up to 7.58% and 6.24%, respectively. Besides, the OAA-SSVM model can improve the feature sparsity. Results show that the proposed method is effective in detecting multiple faults of axial piston pumps.

A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a crucial issue. Therefore, piston wear detection and feature selection are essential and urgent. In this paper, we propose a vibration signal-based methodology using the improved spare support vector machine, which can integrate the feature selection into the piston wear detection learning process. Forty features are defined to capture the piston wear signature in the time domain, frequency domain, and time–frequency domain. The relevance and impact of sparsity in 40 features are illustrated through the single and multiple statistical feature analysis. Model performance is assessed and the sparse features are discovered. The maximum model testing and training accuracy are 97.50% and 96.60%, respectively. Spare features s₁₀, s₁₂, E_w(8), x₇, E_e(5), and E_e(4) are selected and validated. Results show that the proposed methodology is applicable for piston wear detection and feature selection, with high model accuracy and good feature sparsity.