舰船电子工程2024，Vol.44Issue(1)：145-151,7.DOI:10.3969/j.issn.1672-9730.2024.01.029
基于卷积自注意力网络的机械设备故障诊断方法
Convolutional Self-attention Network-based Fault Diagnosis Method of Mechanical Equipment
摘要
Abstract
Deep learning-based intelligent faults diagnosis methods for mechanical equipment rely on a large amount of la-beled samples.However,it is usually difficult to obtain enough samples in engineering,which makes it difficult for deep learning methods to extract fault features completely and seriously affects the generalization and robustness of fault diagnosis models.There-fore,the paper proposes a fault diagnosis method of mechanical equipment under small samples based on convolutional self-atten-tion network and prior knowledge.The designed convolutional self-attention network can automatically learn sample features and fuse the sample multidimensional features obtained from prior knowledge during the training process,with the aim of reducing the number of samples required for model training and improving the fault diagnosis accuracy of mechanical equipment in the case of small samples.Finally,the proposed method is validated with a hydraulic screw pump dataset.The experimental results show that the proposed method achieves 97.5%fault diagnosis accuracy under small samples,and the performance is better than the current common deep learning methods.关键词
深度学习/先验知识/自注意力机制/小样本/故障诊断Key words
deep learning/prior knowledge/self-attention mechanism/small sample/fault diagnosi分类
机械制造引用本文复制引用
李子睿,崔晓龙,王超,张文俊,吴军..基于卷积自注意力网络的机械设备故障诊断方法[J].舰船电子工程,2024,44(1):145-151,7.基金项目
工信部高质量专项重点项目(编号:TC210804R-1) （编号:TC210804R-1）
国家自然科学基金面上项目(编号:51875225) （编号:51875225）
湖北省自然科学基金重点类项目(编号:2021CFA026)资助. （编号:2021CFA026）
