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Wu Huiyong¹, Jiang Kuan², Wang Yanyu¹

¹College of Science, Shenyang University of Chemical Technology, Shenyang, 110142, Liaoning, China.

Scientific reports

|August 6, 2025

概括

本研究介绍了一种先进的DenseNet-Transformer模型,用于诊断轴承故障. 使用快速里叶变形-变化模式分解 (FFT-VMD) 和注意力机制,它在故障分类中达到99.68%的准确性.

科学领域:

机械工程机械工程
信号处理信号处理
人工智能的人工智能

背景情况:

轴承故障在旋转机械中至关重要,影响设备的可靠性.
准确的故障诊断对于预测性维护和运行安全至关重要.
传统的方法经常与复杂的振动信号和微妙的故障指示器作斗争.

研究的目的:

开发一个强大的,高度准确的轴承故障诊断模型.
为了增强从复杂的轴承振动信号中提取特征.
改进故障数据中的时间依赖性建模.

主要方法:

使用快速里埃转换 (FFT) 和变化模式分解 (VMD) 进行信号分解.
将卷积块注意模块 (CBAM) 集成到DenseNet中,以增强功能提取.
使用具有自我注意的变压器模块用于时间序列依赖模型的应用.
优化特征表示多样性和歧视力.

主要成果:

拟议的DenseNet-Transformer模型在轴承故障诊断方面实现了99.68%的分类准确性.
在识别各种轴承故障类型方面表现出卓越的性能.
在不同的操作条件下表现出极好的概括能力.

结论:

基于FT-VMD的DenseNet-Transformer模型为轴承故障诊断提供了一个有效和可靠的解决方案.
注意力机制的整合大大提高了特征提取和诊断准确度.
这种方法为化学设备和其他旋转机械的健康监测提供了有价值的工具.

关键词:

注意力机制注意力机制轴承发生故障这就是为什么CBAM是CBAM.快速的里埃转换是快速的功能提取功能提取变化模式分解的变化模式分解

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