基于ConvNeXt网络的滚动轴承的新型智能故障诊断方法,具有改进的DenseBlock
Jiahao Song1, Xiaobo Nie1, Chuang Wu1
1College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
本研究引入了一种使用并行网络的新型滚动轴承故障诊断方法. 它有效地诊断轴承故障,即使在噪音严重的情况下,数据有限.
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:
- 滚动轴承对于机器的运行至关重要,需要可靠的故障诊断.
- 目前的一维数据驱动的方法与噪音和有限的样本作斗争.
- 现有的技术在具有挑战性的操作环境中缺乏稳定性.
研究的目的:
- 开发一个先进的故障诊断方法,用于滚动轴承.
- 克服现有方法在杂和低样本场景中的局限性.
- 为了提高旋转机械的诊断准确性和可靠性.
主要方法:
- 一个新的并行网络,结合了ConvNeXt和改进的DenseBlock.
- 功能融合将全球和详细的信号功能整合起来.
- 整合Dy-ReLU函数以促进泛化和融合.
主要成果:
- 拟议的方法在高噪音环境中显示出强大的诊断能力.
- 即使训练样本不足,也可以实现有效的故障诊断.
- 对比实验验证实了该方法的稳定性和优越性.
结论:
- 开发的网络为滚动轴承故障诊断提供了强大的解决方案.
- 这种方法在不利条件下显著改善了诊断性能.
- 该方法为工业应用中的预测性维护提供了可靠的工具.
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