基于仿真数据的旋翼系统故障识别  

Rotor system fault diagnosis based on simulation data

作　　者：杨茂[1] 李小龙[1] 

机构地区：[1]西北工业大学航天学院,陕西西安710072

出　　处：《浙江大学学报（工学版）》2013年第12期2188-2194,共7页Journal of Zhejiang University：Engineering Science

摘　　要：采用广义回归神经网络(GRNN)对3种直升机旋翼故障(配平调整片误调、变距拉杆误调、质量不平衡)进行识别.采用三层网络分别识别旋翼故障的类型、位置和程度.网络训练集和测试集采用基于耦合的旋翼-机身仿真结果(包含故障旋翼响应、桨毂载荷、机身振动水平).为提高网络泛化能力,在仿真结果中添加了噪声.结果表明:1)训练好的GRNN能从包含噪声的直升机响应中对故障做出识别,使用仿真数据训练的GRNN可用于旋翼健康和使用监测系统(HUMS)的开发;2)使用包含噪声的数据训练网络能显著提升GRNN的泛化能力;3)合理选择网络扩展常数对于预测准确性非常重要.General regression neural network（GRNN）was used to diagnose three types of rotor system faults,namely,misadjusted trim-tab,misadjusted pitch control rod,and imbalanced mass.Three cascaded levels of networks were used to identify fault type,location,and extend,respectively.Simulation results, which include faulty rotor responses,hub loads,and fuselage vibration,from a coupled rotor-fuselage analytical model were used for training and testing.Artificial noises were added to simulation data to enhance network generality.Results show that：1）trained GRNN is capable of diagnosing faults from noisy helicopter responses,which indicating the feasibility of simulation data-trained GRNN being used in rotor health and usage monitoring system（HUMS）development;2）using noise-added training data can significantly improve GRNN＇s generality;3）properly selecting the spread of network is important for fault diagnosis accuracy.

关 键 词：广义回归神经网络 旋翼系统 故障识别 健康和使用监测系统 

分 类 号：V275.1[航空宇航科学与技术—飞行器设计]