A Condition Monitoring Method of Hydraulic Gear Pumps Based on Multilevel Mechanism-Data Fusion

Abstract

Pumps are important components in aviation fuel hydraulic systems, and thanks to the development of sensor technology and industrial intelligence technology, it is possible to achieve efficient state monitoring of pumps. However, when data quality is poor or the amount of data is small, a single data-driven model may not be able to meet diagnostic accuracy. A condition monitoring method for hydraulic gear pumps based on mechanism-data fusion is proposed. The method combines a mechanism model based on the volumetric efficiency formula with a data-driven model based on vibration signals. First, the parameters of volumetric efficiency are solved by fitting the pressure–flow relationship. Subsequently, a multichannel fusion and multikernel function-weighted ensemble support vector classification (MCMK-SVC) is developed, to establish a data-driven model. Finally, through data-level fusion, feature-level fusion, and decision-level fusion, a condition monitoring model based on mechanism-data fusion is built. Experimental verification shows that the accuracy of the three levels of fusion models exceeds 96.9%. Compared to the single data-driven model or other traditional data-driven models, the accuracy of the proposed method has improved by 3% to 33%, demonstrating the effectiveness of the mechanism-data fusion model.