摘要
机电作动器已经用于民航客机飞控舵面的控制,机电作动系统中传感器输出的正确性对系统正常工作影响较大,因此对传感器进行快速有效的故障检测非常必要。分析机电作动系统的传感器故障模式,针对当前故障检测在处理液态与非液态故障时所需信息多、检测时间长等问题,设计二级GA-BP神经网络对传感器故障模式进行诊断,对比分析了不同训练方式的神经网络方法,确定使用莱温伯格-马夸特学习方法的神经网络的故障诊断分类结果更加准确,并通过遗传算法对神经网络进行了优化。最后通过机电作动系统仿真实验平台验证该方法的有效性。创新之处在于采用了二级网络架构,快速检测液态故障与非液态故障,有效减少了网络故障检测的所需信息量和故障检测时间。
Electromechanical actuator has been used in the control of flight control surface of civil aircrafts.As a key component of electromechanical actuator system,the correctness of the sensor output has a great influence on the operation of the system.There⁃fore,fast and effective fault detection for sensor is very necessary.The sensor types and failure modes of electromechanical actuation system were analyzed.Aiming at the current problems that the fault detection needs a lot information and long detection time to deal with liquid and non⁃liquid faults,two⁃level GA-BP neural network was designed to diagnose and identify sensor faults,and different training methods were compared.It was determined that the fault diagnosis classification results of neural network using Levenberg-Marquardt learning method are more accurate,and then the neural network was optimized by genetic algorithm.Finally,the effective⁃ness of the method is verified by the experimental platform of the electromechanical actuation system.The innovation lies in the use of a two⁃level network architecture,by which liquid faults and non⁃liquid faults can be detected rapidly,so the amount of information re⁃quired for fault detection and the detection time are reduced effectively.
作者
白玉轩
杨建忠
孙晓哲
戴闰志
黄铭媛
BAI Yuxuan;YANG Jianzhong;SUN Xiaozhe;DAI Runzhi;HUANG Mingyuan(Airworthiness College,Civil Aviation University of China,Tianjin 300300,China;Tianjin Key Laboratory of Civil Aircraft Airworthiness and Maintence,Civil Aviation University of China,Tianjin 300300,China;China Civil Aviation Shanghai Aircraft Airworthiness Certification Center,Shanghai 200050,China)
出处
《机床与液压》
北大核心
2021年第15期188-194,共7页
Machine Tool & Hydraulics
基金
大飞机重大专项
中国民航大学科研启动基金(2011QD15X)。
关键词
机电作动系统
传感器
神经网络
故障诊断
Electromechanical actuation system
Sensor
Neural network
Fault diagnosis
