DSMDTN:一个数据选择性的多级双传输网络,用于旋转机械关键部件的故障诊断
IEEE transactions on cybernetics
|February 18, 2026
概括
一个新的数据选择性多级双传输网络 (DSMDTN) 改善了滚动轴承故障诊断. 这种方法提高了诊断准确度和跨不同工作条件和噪音环境的传输能力.
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:
- 传统的深度学习模型在不同的工作条件和噪音下难以进行轴承故障诊断.
- 可靠的故障诊断对于旋转机械的维护和运行安全至关重要.
研究的目的:
- 为改进滚动轴承故障诊断提出一个数据选择性的多级双传输网络 (DSMDTN).
- 改善在各种操作条件和噪音环境中的诊断性能.
- 为了使轴承故障分类的强大的转移学习.
主要方法:
- 数据选择器 (DS) 模块使用数学,和异常分数识别高质量的源样本.
- 一个多尺度连接U-Net (MCU-Net) 使用封闭的卷积块提取多尺度,域不变的特征.
- 双分类器 (DC) 模块使用单独的源分类器和目标分类器,最大限度地降低了分布差异和分类损失.
主要成果:
- 与最先进的模型相比,DSMDTN表现出卓越的准确性和传输能力.
- 在CWRU和PT数据集上验证了实验,在各种传输任务下显示出强大的性能.
- 该模型有效地处理了不同的噪声水平,提高了诊断可靠性.
结论:
- 拟议的DSMDTN显著提高了滚动轴承故障诊断的准确性和可转移性.
- 数据选择,多尺度特征提取和双重分类的整合提供了一个强大的解决方案.
- 在具有挑战性的工业环境中,DSMDTN为智能故障诊断提供了有前途的方法.
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