Affiliation:
1. The School of Mechanical and Electrical Engineering Changchun University of Technology Changchun China
2. The School of Mechanical and Electrical Engineering Changchun Institute of Technology Changchun China
3. The School of Aeronautical Engineering Jilin Institute of Chemical Technology Jilin China
4. Aviation University of the Air Force Changchun China
Abstract
AbstractAs the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment.
Funder
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)