基于注意力机制和格雷厄姆角场的滚动轴承故障诊断的深度学习方法
Jingyu Lu1, Kai Wang1,2, Chen Chen1,2
1Department of Mechanical Engineering, Jiangnan University, Wuxi 214122, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一种使用格拉米安角场 (GAF) 编码和改进的ResNet50模型的新滚动轴承故障诊断方法. GAF-ResNet50方法提高了确定轴承故障的准确性和及时性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 由于巨大的数据量,传统的滚动轴承故障诊断方法在准确性和及时性方面扎.
- 有效的故障诊断对于工业设备的可靠性和维护至关重要.
研究的目的:
- 开发一个准确和及时的滚动轴承故障诊断方法.
- 利用格拉米安角场 (GAF) 编码和改进的ResNet50模型来增强诊断能力.
主要方法:
- 用GAF编码将一维的振动信号转化为二维的特征图像.
- 改进的ResNet50模型被用于自动特征提取和故障类型的分类.
- 拟议的方法使用来自Case Western Reserve大学的滚动轴承数据进行了验证.
主要成果:
- 与其他智能算法相比,GAF-ResNet50方法实现了更高的分类准确性.
- 提出的方法在故障诊断方面表现出卓越的及时性.
- 成功地对不同类型的滚动轴承故障进行了分类.
结论:
- 基于GAF编码和改进的ResNet50的建议故障诊断方法是有效和高效的.
- 这种技术为准确和及时的滚动轴承故障诊断提供了一个有前途的解决方案.
- 该研究强调了深度学习和先进信号处理在机器健康监测方面的潜力.
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