福建电脑2024，Vol.40Issue(5)：33-37,5.DOI:10.16707/j.cnki.fjpc.2024.05.006
应用混合2D-CNN-LSTM模型诊断轴承故障
Application of Hybrid 2D-CNN-LSTM Model for Diagnosing Bearing Faults
摘要
Abstract
Rolling bearings are the core components in mechanical equipment.To ensure the accuracy and real-time diagnosis of rolling bearing faults,this paper proposes a hybrid deep model fault diagnosis method.This method combines two-dimensional convolutional neural networks and long short-term memory networks,not only improving the performance of the model,but also accurately capturing the spatial and temporal features in bearing signals.The experimental results show that this method can accurately classify bearing faults and achieve real-time monitoring of bearing operation status.关键词
二维卷积神经网络/长短期记忆网络/轴承故障/分类识别Key words
2D-CNN/LSTM/Bearing Fault/Classification Recognition分类
信息技术与安全科学引用本文复制引用
江跃龙..应用混合2D-CNN-LSTM模型诊断轴承故障[J].福建电脑,2024,40(5):33-37,5.基金项目
本文得到广东省普通高校创新团队项目(自然科学)(No.2021KCXTD068)、广州市高等教育教学质量与教学改革工程计算机应用技术专业群"双师型"教师培养培训基地(No.2022SSPRJD004)、科教融合专项工艺改进项目基于改进的深度学习网络芯片引脚及表面缺陷检测方法(No.GTXYK2208)资助. （自然科学）
