滚动轴承故障诊断基于格拉米安角差场和改进的通道注意力模型
Lunpan Wei1, Xiuyan Peng1, Yunpeng Cao2
1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang, China.
PeerJ. Computer science
|January 23, 2024
概括
本研究介绍了一种改进的方法,用于滚动轴承故障诊断,使用格拉米安角差异场和剩余网络,以频道为重点. 这种新的方法显著提高了诊断的准确性,即使在不平衡的数据集.
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:
- 滚动轴承故障诊断对于工业机械维护至关重要.
- 传统的卷积神经网络 (CNN) 在准确诊断由振动信号引起的轴承故障方面存在局限性.
研究的目的:
- 为了提高滚动轴承故障诊断的准确性和通用性.
- 提出一种新的方法,将格拉米安角差异场 (GADF) 和残余网络 (ResNet) 与频道注意 (Fca) 模块相结合.
主要方法:
- 振动信号被转换成RGB图像使用GADF进行数据预处理.
- 在ResNet架构中嵌入了一个频道注意模块 (Fca),结合了离散的等号变换 (DCT).
- Fca模块的设计是为了有效地分析故障图像中的通道信息,并识别特有的故障特征.
主要成果:
- 拟议的模型在平衡的数据集上实现了99.3%的诊断准确率.
- 该模型保持了高准确率98.6%,即使在不平衡的数据集上进行评估.
- 实验结果显示,故障诊断的准确性和模型的概括性有了显著的改善.
结论:
- 整合GADF,ResNet和Fca为滚动轴承故障诊断提供了一种优越的方法.
- 该方法有效地解决了传统CNN的局限性,在不同的数据条件下提供了强大的性能.
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