摘要
针对航空发动机的一些常见故障类型,为了达到进行有效识别的目的,提出了一种基于小波包和神经网络相结合的发动机故障诊断方法。以某型航空发动机为研究对象,通过小波包对采集到的振动数据进行分解和重构,提取出表征发动机工作状况的特征向量,并将其作为训练样本数据和检验样本数据,输入小波Elman神经网络中并对其进行训练,试验结果表明:这种诊断模型能够有效地识别出所研究的航空发动机故障类型,故障诊断率较高。
In order to effectively identify the common fault types of aviation engine, a new method of engine fault diagnosis based on wavelet packet and neural network is proposed. Taking a certain type of aero-engine as research object, the vibration signal is decomposed and reconstructed through wavelet packet to obtain feature vector of its working condition. Then these data are input into the wavelet Elman neural network as training sample data and test sample data. Experimental results show that this method is feasible and the fault types of aero-engine is well recognized
作者
杨永刚
顾杰
YANG Yonggang GU Jie(Sino-Europewn Institute of Aviation Engineering, CA UC, Tianjin 300300, Chin)
出处
《中国民航大学学报》
CAS
2016年第5期9-13,共5页
Journal of Civil Aviation University of China
基金
国家自然科学基金项目(61172013)
关键词
小波变换
神经网络
航空发动机
故障诊断
wavelet transform
neural network
aero-engine
fault diagnosis
