滚动轴承故障诊断方法基于SWT和改进的视觉变压器
1Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究引入了一种新方法,用于诊断滚动轴承故障,使用同步波波变换 (SWT) 和增强视觉变换器 (ResCAA-ViT). 该方法在各种操作条件下实现了高精度的故障检测.
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机器健康监测至关重要.
- 现有的方法在不同的操作条件下难以准确.
- 需要先进的信号处理和深度学习架构.
研究的目的:
- 开发一种新且准确的滚动轴承故障诊断方法.
- 在恒定和变化的负载条件下提高诊断准确度.
- 创建一个强大的模型,适用于现实世界的工业场景.
主要方法:
- 同步波形变换 (SWT) 用于增强时间频率信号表示.
- 一个增强的视觉变压器 (ResCAA-ViT) 架构,集成CNN和变压器.
- 使用ResNet50转移学习,双分支注意力机制 (CAA) 和组合损失函数.
主要成果:
- 在恒定负载下,在Case Western Reserve大学数据集上实现了99.96%的平均准确性.
- 在相同的数据集上在不同的负载条件下达到96.51%的平均准确性.
- 在真实世界的风力轮发电机轴承数据集上证明了99.25%的平均准确性.
结论:
- 拟议的SWT和ResCAA-ViT方法显著提高了滚动轴承故障诊断的准确性.
- 混合架构有效地捕获了本地和全球信号特征.
- 该方法显示出强大的通用性和工业故障诊断的实际适用性.
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