基于深度学习的军用飞机部件状态参数预测

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (6) : 61-67.

论文

基于深度学习的军用飞机部件状态参数预测

李军亮1.2 滕克难1 夏菲2.3

作者信息 +

Military aircraft components state parameter prediction using deep belief learning

LI Junliang1.2 TENG Kenan1 XIA Fei2.3

Author information +

文章历史 +

摘要

军用飞机状态监测数据具有多维性、非线性和强耦合性的特点，监测数据样本容量大、更新速率高，现有的状态预测模型难以满足工程中高精度和实时性的要求。因此如何准确、快速的预测军用飞机的状态信息是目前亟需解决的难题。论文基于深度学习理论和模拟退火算法构建了军用飞机关键部件的状态参数预测模型。主要研究过程由三个步骤实现：一是选择不同任务剖面下的飞机关键部件状态信息的监测参数，以此来设计网络样本；二是构建深度学习网络和预测模型；三是对构建模型的检验和验证。以某型发动机在飞行过程的监测数据为例，分别将本文设计方法和支持向量机（SVM）和ELMAN网络预测结果进行比较发现，基于DBN网络状态预测模型的预测值和实测值的平均误差小于0.04，明显小于SVM和ELMAN方法，验证了该方法在复杂系统状态信息多步预测中的有效性和可行性。

Abstract

Military aircraft state monitoring data have the characteristics of multidimensionality, nonlinearity and strong coupling, and monitoring data samples are of large capacity with high updating rate. So, existing state prediction models are difficult to meet the engineering requirements of high precision and realtime performance. How to predict military aircraft status informations accurately and fast is a difficult problem which needs to be settled. The paper uses the deep belief learning theory and simulated annealing algorithm to present a state prediction model for military aircraft key components. The research process consists of three stages: choosing monitoring parameters of the aircraft key components under different mission profile in order to design network samples; developing a deep learning network and prediction model; validating the model with testing date. Lastly, an aircraft engine was employed to demonstrate the efficacy of the proposed approach. The predicted results were compared with those by the SVM and ELMAN. It is found that the average error between the predicted and the measured values based on the proposed approach is less than 0.04, much smaller than that of the SVM and ELMAN method, and its running time is shorter than the above two methods, which proves the feasibility and effectiveness of the proposed approach.

导出引用

李军亮1.2 滕克难1 夏菲2.3. 基于深度学习的军用飞机部件状态参数预测[J]. 振动与冲击, 2018, 37(6): 61-67

LI Junliang1.2 TENG Kenan1 XIA Fei2.3. Military aircraft components state parameter prediction using deep belief learning[J]. Journal of Vibration and Shock, 2018, 37(6): 61-67

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