强背景噪声条件下自适应图卷积神经网络的航空发动机附件机匣故障诊断方法  

Fault diagnosis for aero-engine accessory gearbox by adaptive graph convolutional networks under intense background noise conditions

作　　者：余晓霞 汤宝平[1] 魏静[1] 邓蕾[1] Yu Xiaoxia;Tang Baoping;Wei Jing;Deng Lei(State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400044

出　　处：《仪器仪表学报》2021年第8期78-86,共9页Chinese Journal of Scientific Instrument

基　　金：重庆市自然科学基金(cstc2019jcyj-zdxmX0026);国家重点研发计划(2020YFB1709800);国家自然科学基金(51775065)项目资助。

摘　　要：针对强背景噪声条件下航空发动机附件机匣故障难以诊断的问题,提出了自适应图卷神经网络(AGCNet)航空发动机附件机匣故障诊断方法。将航空发动机附件机匣振动信号通过小波包进行分解,并将小波包系数矩阵定义为包含节点与边的图。在图卷积神经网络中构建自适应图卷积核,基于切比雪夫多项式设计了一种自适应图卷积操作,通过自适应图卷积核对图中节点与边进行特征提取,增强模型在强噪声条件下的泛化性。最后利用全连接层进行特征抽取,进而实现航空发动机附件机匣故障。应用案例表明所提自适应图卷积神经网络模型(AGCNet);在强背景噪声条件下对航空发动机附件机匣故障平均诊断精度为86.42%,均高于LeNet、ResNet以及GCNet模型。能够有效识别故障,可应用于航空发动机附件机匣故障诊断。An adaptive convolutional graph neural network fault diagnosis method is proposed to diagnose aero-engine accessory gearbox faults under intense background noise conditions.Wavelet packet coefficient matrixes,which decompose from the gearbox′s vibration signals by wavelet packets,are defined as graphs containing nodes and edges.An adaptive graph convolution operation is designed based on Chebyshev polynomials,the adaptive graph convolutional kernel is constructed in the graph convolutional networks to improve the fault feature extraction ability of nodes and edges,and enhance the generalization of the model under strong noise conditions.Finally,the fully connected layer is used for feature extraction to achieve fault diagnosis of aero-engine accessory gearbox.The application case shows that the proposed the adaptive graph convolutional networks has an average diagnostic accuracy of 86.42%for aero-engine accessory magazine fault diagnosis under strong background noise,which is higher than LeNet,ResNet and GCNet models,and can effectively identify faults and be applied to aero-engine accessory magazine fault diagnosis.

关 键 词：航空发动机附件机匣 自适应图卷积神经网络 强背景噪声 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化]