基于增强的YOLO v8和集成注意力机制的旋转机械部件的故障诊断方法
Xiaoguang Wang1,2,3, Laohu Yuan2, Le Ma1,2
1School of Aviation, Anyang Vocational and Technical College, Anyang, China.
PloS one
|December 12, 2025
概括
本研究介绍了一种改进的YOLO v8模型,用于旋转机械故障诊断. 改进的方法实现了近乎完美的准确性,大大提高了对组件故障的检测.
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:
- 准确的故障诊断对于旋转机械的安全操作至关重要.
- 传统方法在检测小故障特征和提取过程中信息丢失方面存在困难.
- 现有的深度学习模型在用于旋转机械故障诊断的高级特征提取方面面临挑战.
研究的目的:
- 为旋转机器提出一个改进的YOLO v8故障诊断方法 (YOLO v8-C-OD).
- 增强检测小目标故障特征,防止信息丢失.
- 提高旋转机械故障诊断的整体准确性和可靠性.
主要方法:
- 振动信号被转换成时间频率图像使用连续波波变换 (CWT) 与莫莱特波波.
- 在YOLO v8中,C2F模块得到了全维动态卷积 (ODConv) 的增强,以改善特征提取.
- 整合了卷积块注意模块 (CBAM),以改进特征选择并提高诊断准确度.
主要成果:
- 拟议的YOLO v8-C-OD方法在CWRU数据集上实现了100%的准确性.
- 该方法在江苏Qianpeng诊断工程公司数据集上达到99.75%的准确性.
- 实验结果表明,与现有最先进的模型相比,性能优越.
结论:
- YOLO v8-C-OD 方法显著提高了旋转机械故障诊断的准确性.
- 整合CWT,ODConv和CBAM增强了特征提取和诊断能力.
- 这种新的方法为工业和飞机旋转机械故障诊断提供了有价值的参考.
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