A Spare Support Vector Machine Based Fault Detection Strategy on Key Lubricating Interfaces of Axial Piston Pumps

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.