摘要
为有效解决航空发动机的故障诊断难题,提出了基于深度自编码网络的航空发动机故障诊断方法,对发动机进行故障诊断技术研究。首先,对监测数据进行预处理,根据数据特征构建深度自编码网络的基本结构,采用无标签数据样本集对深度自编码网络进行预先训练,得到网络参数的初始值;其次,利用有标签的数据样本集对该网络进行训练,对网络参数进行微量调整,创建基于深度自编码神经网络的航空发动机故障诊断模型;最后,采用含有标签的测试样本集对创建的故障诊断模型进行诊断测试。为了表明所提出方法的优越性,将本研究方法与其他几种常用故障诊断方法的故障诊断结果进行了对比。结果表明,与反向传播神经网络、径向基神经网络等常用的故障诊断方法相比,所提出方法的诊断正确率更高,诊断效果更好。
The fault diagnosis of an aero-engine always plagues the industry due to its complex internal structure.In light of this problem,this paper proposes a fault diagnosis method based on the deep self-coding network.First,the monitoring data is preprocessed,and the structure of the network is constructed according to the da⁃ta's features.Then,the unlabeled data samples train the network for the initial values of its parameters,and the labeled samples work for a slight adjustment.Thus,the aeroengine fault diagnosis model based on deep self-en⁃coding neural network is established.Finally,the proposed model presents its advantages over common fault di⁃agnosis methods,including back propagation neural network and radial basis neural network,in accuracy accord⁃ing to the tested of labeled samples.
作者
崔建国
李国庆
蒋丽英
于明月
王景霖
CUI Jianguo;LI Guoqing;JIANG Liying;YU Mingyue;WANG Jinglin(School of Automation,Shenyang Aerospace University Shenyang,110136,China;Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management Shanghai,201601,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2021年第1期85-89,201,202,共7页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51605309)
航空科学基金资助项目(201933054002,20163354004)
辽宁省教育厅基金资助项目(JYT2020021)。
