Health assessment method of nose landing gear hydraulic retraction/extension system based on GRNN
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摘要:
随着民用飞机健康管理技术的不断发展，飞机重要系统及部件的状态监测数据不断丰富。前起落架液压收放系统的健康状态对飞机起降的影响较大，该系统虽然具备多维监测参数，但难以有效利用系统监测数据准确评估其健康状态。基于此，针对前起落架液压收放系统健康评估问题，通过AMESim软件建模，构建系统的仿真模型，研究前起落架收放性能受液压元件参数性能变化的影响。使用前起落架收放作动筒不同等级故障时的性能数据作为原始数据，提取收上时间、流速最大值等表征参数，提出基于广义回归神经网络(GRNN)的健康指数构造方法，可以更加有效地对该系统的健康状态进行评估，通过方法对比证明了其有效性和准确性。
Abstract:The condition monitoring data of critical aircraft systems and components has been continuously improved with the ongoing advancement of civil aircraft health management technology.The health status of the nose landing gear hydraulic retraction/extension system has a great influence on the take-off and landing of the aircraft. Although the system has multi-dimensional monitoring parameters, it is difficult to effectively use the monitoring data to accurately evaluate its health status. Aiming at the health assessment of the landing gear hydraulic retraction/extension system of a domestically developed regional aircraft, this paper constructs a simulation model of the system through AMESim, so as to study the influence of the working performance of the system due to the change of component parameter performance in the circuit. Previously, the performance data of different levels of the landing gear retraction/extension cylinder were the original data. In order to better assess the system’s health, a health index generation approach based on the generalized regression neural network (GRNN) was put forth after the feature parameters, such as the closing time and the maximum flow rate, were retrieved. The effectiveness is verified by comparing the results with other methods.
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图 1 前起落架液压收放系统健康评估方案框图
Figure 1. Health assessment scheme frame of nose landing gear hydraulic retraction/extension system
图 2 前起落架液压收放系统原理示意图
Figure 2. Schematic diagram of nose landing gear hydraulic retraction/extension system
图 3 前起落架液压收放系统液压回路仿真模型
1. 油箱；2. EDP 2A；3. 油滤；4. 卸压阀；5. 单向阀；6. 蓄压器；7. 起落架选择阀；8. 前起开锁作动筒；9. 节流阀；10. 前起收放作动筒；11. 梭阀。
Figure 3. Simulation model of hydraulic circuit of nose landing gear hydraulic retraction/extension system
图 4 无故障状态下各参数仿真结果
Figure 4. Simulation results of each parameter with no failure operation
图 5 不同泄漏程度下的参数响应曲线
Figure 5. Response curves of different parameters under different leakage degrees
图 8 不同$\sigma $下的评价指标曲线
Figure 8. Curves of evaluation indicators under different $\sigma $ values
表 1 部分训练集数据
Table 1. Parts of training set data
样本 $ {x_{1,1}} $ $ {x_{1,2}} $ $ {x_{1,7}} $ $ {x_{1,11}} $ $ {x_{1,27}} $ $ {x_{1,31}} $ $ {y_{{\text{train}}}} $ 1 7.55 9.60 205.539 0 −5.688 1.584 0 2 8.10 8.17 172.257 8.297 −4.100 1.659 0.06 3 10.47 5.62 127.200 12.511 −2.146 2.502 0.25 4 11.32 5.21 123.445 12.800 −1.843 2.560 0.30 5 7.64 9.33 203.040 2.000 −5.387 1.587 0.01 6 9.21 6.58 138.609 11.590 −2.862 2.318 0.16 $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ 
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表 2 各方法评价指标对比
Table 2. Comparison of evaluation indicators of various methods
方法 MAE RMSE MAPE/% SMAPE/% 多元非线性拟合 0.0253 0.03908 19.1621 17.5161 BPNN 0.0218 0.02350 26.2809 21.3723 GRNN 0.0031 0.00420 2.3901 2.3926
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