文献类型：期刊文献

中文题名：基于改进注意力机制的CNN的齿轮箱故障诊断

英文题名：Gearbox Fault Diagnosis Based on CNN with Improved Attention Mechanism

作者：邵浙梁[1];戚知宽[1];周邵萍[1]

机构：[1]华东理工大学承压系统与安全教育部重点实验室,上海200237

年份：2024

卷号：50

期号：6

起止页码：920

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：;Scopus;北大核心:【北大核心2023】；CSCD:【CSCD_E2023_2024】；

语种：中文

中文关键词：故障诊断;卷积神经网络;注意力机制;软阈值激活函数;齿轮箱

外文关键词：fault diagnosis;convolutional neural network;attention mechanism;soft threshold activation function;gearbox

摘要：针对实际工况中齿轮箱振动信号复杂多变,导致传统基于卷积神经网络(Convolutional Neural Networks,CNN)的齿轮箱故障诊断方法存在诊断精度不高、训练收敛性能差等问题,首先,提出一种改进的注意力机制和一种基于注意力机制的软阈值激活函数,在此基础上,构建基于改进注意力机制的CNN故障诊断模型;然后,通过齿轮箱开源数据集的实验数据,验证改进的注意力机制和基于注意力机制的软阈值激活函数能否有效提升模型的诊断精度和训练收敛性能;最后,将构建的模型应用于实际工况齿轮箱的故障诊断。结果表明,构建的模型满足某企业齿轮箱出厂检测的需求,在诊断精度和训练收敛性等方面具有优势。
Considering the complexity and variability of the vibration signals from gearboxes under actual operating conditions,which contribute to low diagnostic accuracy and suboptimal training convergence in traditional convolutional neural network-based gearbox fault diagnosis methods,we propose an enhanced attention mechanism along with a soft threshold activation function derived from this mechanism.Building upon these innovations,we develop a convolutional neural network fault diagnosis model that incorporates the improved attention mechanism.Subsequently,through experiments utilizing data from an open-source gearbox dataset,we demonstrate that both the enhanced attention mechanism and the soft threshold activation function significantly enhance diagnostic accuracy and training convergence performance of the model.Finally,we apply this constructed model to real-world gearbox fault diagnosis scenarios.Results indicate that it fulfills enterprise factory inspection requirements while exhibiting superior diagnostic accuracy and training convergence.