一种基于改进的平行一维卷积神经网络的滚动轴承故障诊断方法
Hongwei Bai1, Weiyan Tong1, Zhenkun Geng1
1School of Chemical Process Automation, Shenyang University of Technology, Liaoyang, China.
PloS one
|August 11, 2025
概括
这项研究引入了用于滚动轴承故障诊断的先进神经网络,达到99.62%的准确性. 改进的模型提高了设备的可靠性,即使在噪音条件下也能准确检测故障.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 滚动轴承故障诊断对于工业设备的可靠性和防止停机时间至关重要.
- 现有的方法在信号噪声比较低的环境中难以准确,通常低于92%.
研究的目的:
- 开发一个改进的深度学习模型,用于准确的滚动轴承故障诊断.
- 在噪音条件下克服现有方法的局限性.
主要方法:
- 提出了一个改进的平行一维卷积神经网络 (CNN).
- 集成了一个并行的双通道卷积内核,封闭的循环单元 (GRU) 和注意力机制.
- 使用全球最大共享和软max进行分类.
主要成果:
- 实现了高超的故障诊断准确率99.62%.
- 与传统的CNN和基准方法相比,表现显著改善.
- 该模型有效地捕捉全球和本地特征,同时减轻过拟合和参数冗余.
结论:
- 拟议的模型为滚动轴承故障诊断提供了一个高度有效的解决方案.
- 双通道CNN,GRU和注意力机制的整合提高了诊断的准确性和可靠性.
- 这种方法显著提升了工业设备状态监测的最新技术.
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