Abstract
Abstract In order to realize the safety status monitoring and health management of aeroengine fuel system, a performance degradation detection method of aeroengine fuel metering device was proposed. Aiming at the internal leakage, external leakage, static friction increase, dynamic friction increase, differential pressure controller degradation, and other common performance degradation modes of fuel metering devices, a residual life estimation method based on random forest support vector regression (RF-SVR) was proposed. The SVR model optimized by RF feature selection is used to estimate the remaining life of components. The simulation results show that the mean square error of remaining useful life (RUL) estimation is less than 1.8, the average percentage error is less than 3%, and it has high prediction accuracy. Therefore, the evaluation and verification of the internal leakage health indicators proposed in this article screen out the health indicators that are sensitive to changes in performance degradation parameters but insensitive to changes in environmental and structural parameters and provide decision-making reference for onsite maintenance of engine fuel metering devices.
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