国外文献 > 1.轴承 > 5. 监测与诊断 > 故障诊断
关键词搜索：
| 标 题： |
A BEARING FAULT DIAGNOSIS METHOD BASED ON VGG SENET-BIGRU GLOBAL ATTENTION
基于VGG SENET-BIGRU全局注意力的轴承故障诊断方法 |
| 作 者： |
Qiang Liu
Minghao Chen
Hongxi Lai
Zhengwei Dai
Peirong Chen
Youlin Liang
Mingxin Hou
Xiaoming Xu
Guangbin Wang |
| 关键词： |
BEARING FAULT DIAGNOSIS [轴承故障诊断]
DEEP LEARNING [ 深度学习]
PARALLEL MODULES [ 并行模块]
ATTENTION MECHANISM [ 注意机制] |
| 出版年： |
2024 |
| 文集名： |
14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2024)
第十四届质量、可靠性、风险、维护与安全工程国际会议（QR2MSE 2024） |
| 会议名： |
14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2024)
第十四届质量、可靠性、风险、维护与安全工程国际会议（QR2MSE 2024） |
| 届 次： |
14th |
| 摘 要： |
Rolling bearings are the core devices in the industrial field, widely used in machinery production and automation industry. Therefore, it is necessary to detect the fault of rolling bearing. In this paper, an improved VGG model based on Channel Attention mechanism (SENet) is proposed to extract the spatial features of multi-scale features after fault signal preprocessing. The BiGRU network model based on Global Attention optimization is proposed to extract the time domain features of multi-scale features after fault signal pretreatment. Excellent results are obtained by parallel fusion for fault detection.
滚动轴承是工业领域中的核心部件，广泛应用于机械制造和自动化行业。因此，检测滚动轴承故障具有重要意义。本文提出一种基于通道注意力机制（SENet）的改进型VGG模型，用于在故障信号预处理后提取多尺度特征的空间信息； 同时提出一种基于全局注意力优化的BiGRU网络模型，用于在故障信号预处理后提取多尺度特征的时间域信息。通过并行融合方法，实现了故障检测的优异性能。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
京公网安备 11010202009788