Pressure Signal Prediction of Aviation Hydraulic Pumps Based on Phase Space Reconstruction and Support Vector Machine

Yuan Li,Zhuojian Wang,Zhe Li,Zihan Jiang

doi:10.1109/access.2020.3047988

Yuan Li, Zhuojian Wang + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.3047988

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Abstract

In view of the difficulty of fault prediction for aviation hydraulic pumps and the poor real-time performance of state monitoring in practical applications, a hydraulic pump pressure signal prediction method is proposed to accomplish the monitoring and prediction of the health status of hydraulic pumps in advance. First, based on the on-line real-time acquisition of time series flight parameters and pressure signal data, the chaotic characteristics of the system are analyzed using chaos theory, so that the time series pressure signal is predictable. Second, phase space reconstruction (PSR) of the one-dimensional time series data is conducted. The embedding dimension $m$ and time delay $\tau $ are obtained by the C-C method. The reconstructed matrix is used as the training set and test set of the support vector regression (SVR) algorithm model according to a certain proportion, and the genetic algorithm (GA) is then used to optimize the parameters of the SVR model. Finally, the SVR model optimized by the genetic algorithm based on phase space reconstruction (PSR-GA-SVR) is used to test the test set data. The results show that the prediction accuracy of the proposed method is higher than that of the BP neural network based on phase space reconstruction (PSR-BPNN) and the SVR model based on phase space reconstruction (PSR-SVR). Relative to PSR-BPNN and PSR-SVR, PSR-GA-SVR produces a minimum mean square error (MSE) reduced by 73.40% and 68.0%, respectively, and a mean absolute error (MAE) decreased by 90.41% and 90.87%, respectively. The confidence level for PSR-GA-SVR was increased, and the coefficient of determination was greater than 0.98.

Highlights

The aviation hydraulic pump is the ‘heart’ of an aircraft’s hydraulic system
Wang addressed the problem that the fault mechanism of an axial piston pump is not clear, which leads to difficulties in fault detection
She proposed a multi-fault classification and diagnosis method based on a deep trust network, and the accuracy of the results reached 97.40% [8]. She used a new convolutional neural network based on minimum entropy deconvolution to classify faults in axial piston pump, the results showed that this method is better than the traditional method [9]

Summary

INTRODUCTION

The aviation hydraulic pump is the ‘heart’ of an aircraft’s hydraulic system. Once it fails, it will cause serious consequences, affect flight missions, or even lead to aircraft destruction and human death. It is very time-consuming to conduct accelerated life tests in the use stage, and the set working conditions deviate from the actual situation, which leads to the deviation of the experimental results It is a very economical and reliable method to monitor and predict the hydraulic pump faults using the on-line monitoring data. The results showed that the proposed method can effectively identify the fault mode and has good accuracy [19] He again proposed an SVR method using the Gaussian mixture model to predict the degradation process based on the pump outlet pressure signal, and compared with the existing methods, its prediction effect was better [20]. The following two parameters need to be determined when SVR theory is used for regression prediction: the penalty factor C and the kernel parameter γ

GENETIC ALGORITHM MODEL

Findings

CONSTRUCTION OF PSR-GA-SVR MODEL