机械科学与技术2025，Vol.44Issue(7)：1143-1150,8.DOI:10.13433/j.cnki.1003-8728.20230255
小样本下MTF和CBAM-IGPN的柴油机气门故障诊断
Air Valve Fault Diagnosis of Diesel Engine Based on MTF and CBAM-IGPN with Small Samples
王立佳 1黄国勇2
作者信息
- 1. 昆明理工大学 信息工程与自动化学院,昆明 650504
- 2. 昆明理工大学 信息工程与自动化学院,昆明 650504||昆明理工大学 民航与航空学院,昆明 650504
- 折叠
摘要
Abstract
In response to the issue of unclear early fault characteristics of diesel engine valves and an insufficient number of fault samples leading to low accuracy in fault diagnosis and recognition,this paper proposes a diesel engine valve clearance fault diagnosis method based on markov transition field(MTF)and convolutional attention mechanism module fusion with improved gaussian prototype network(CBAM-IGPN)embedded in the network under small samples.Firstly,based on the characteristics of the vibration signal of the diesel engine cylinder head,the one-dimensional vibration signal of the cylinder head is encoded into a two-dimensional feature map through MTF.Secondly,by improving the embedding network in GPN,the initial convolutional neural networks(CNN)are improved into deep convolutional neural networks(DCNN)to enhance the model's mining of deep information in feature maps.CBAM is added to the convolutional layer of DCNN to enhance the models attention to important regions.Finally,the encoded feature map is input into CBAM-IGPN for training and testing to obtain classification results.The results indicate that the method proposed in this article has higher accuracy in diagnosing diesel engine valve faults under small sample conditions.关键词
柴油机/故障诊断/马尔可夫变迁场/高斯原型网络/小样本Key words
diesel engine/fault diagnosis/markov transition field/gaussian prototype network/small sample分类
能源科技引用本文复制引用
王立佳,黄国勇..小样本下MTF和CBAM-IGPN的柴油机气门故障诊断[J].机械科学与技术,2025,44(7):1143-1150,8.
