基于振动信号的尾桨叶结冰判断方法  

Judgment of tail rotor blade icing based on vibration signal

作　　者：党冰楠 李小路[1] 张武林[1] DANG Bingnan;LI Xiaolu;ZHANG Wulin

机构地区：[1]中国飞行试验研究院,陕西西安710089

出　　处：《现代机械》2025年第1期41-45,共5页Modern Machinery

摘　　要：提出了一种基于直升机多点机体振动变化判定尾桨叶结冰状态的方法。直升机旋翼系统结冰后其质量特性和气动特性均发生改变,对飞行能力和飞行安全产生较大的影响。旋翼为高速旋转的动部件,其结冰状态、结冰厚度等特征难以直接测量。论文通过分析直升机自然结冰飞行试验时尾桨叶结冰状态下机体多个位置在尾桨基频、倍频等频率下的振动变化规律,采用小波变换提取机体各个点的振动能量值,并建立BP神经网络模型判定尾桨叶的结冰状态以及结冰程度。对比神经网络输出值与设定目标输出结果的误差,表明了建立的神经网络模型的准确程度,证明提出的判定方法有效。This paper proposes a method to judge the icing state of helicopter tail rotor blades based on the multi-point fuselage vibration of the helicopter.The mass characteristics and aerodynamic characteristics of the helicopter rotor system change after icing,which has a great impact on flight capability and flight safety.The rotor is a high-speed rotating moving part,and its characteristics such as icing state and icing thickness are difficult to measure directly.In this paper,through analyzing the vibration variation law of multiple positions of the fuselage under the basic frequency and multiple frequency of the tail rotor during the natural icing flight test,the wavelet transform is used to extract the vibration energy value of each point of the fuselage,and the BP neural network model is established to determine the icing state and icing degree of the tail rotor.By comparing the error between the output value of the neural network and the output result of the set target,the accuracy of the established neural network model is verified,which proves that the proposed method is effective.

关 键 词：直升机尾桨结冰 机体振动 小波分析 BP神经网络 

分 类 号：V328.1[航空宇航科学与技术—人机与环境工程]