通过脱蒸和低等级适应来进行轻型轴承故障诊断
Ovanes Petrosian1, Pengyi Li2, Yulong He1
1St.Petersburg State University, 7-9 Universitetskaya Embankment, 199034, St Petersburg, Russia.
Scientific reports
|October 16, 2025
概括
本研究介绍了DKDL-Net,这是一种用于滚动轴承故障诊断的轻量级深度学习模型. 它在显著降低参数和计算复杂性的情况下实现了高精度,改善了工业应用.
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.
- 深度学习和先进的信号处理已经推进了轴承故障诊断.
- 由于特征提取速度和计算复杂性,现有的方法在工业部署中面临挑战.
研究的目的:
- 提出一个轻量级轴承故障诊断模型,DKDL-Net,解决工业限制.
- 为了减少计算复杂性和提高轴承故障检测中的特征提取速度.
- 开发一个高精度和高效的模型,用于滚动轴承故障诊断.
主要方法:
- 使用6层神经网络构建了一个教师模型.
- 使用了脱知识蒸 (DKD) 和低级自适应 (LoRA) 微调.
- 与教师模型 (69,626) 相比,DKDL-Net学生模型的训练参数显著减少 (6,838).
主要成果:
- 在CWRU数据集上,DKDL-Net实现了99.48%的准确性.
- 该模型在较低的参数和计算复杂度下表现出卓越的性能.
- 在准确度方面,DKDL-Net的表现比最先进的 (SOTA) 模型高0.58%.
结论:
- 在工业环境中,DKDL-Net为滚动轴承故障诊断提供了有效和高效的解决方案.
- 由于DKDL-Net的轻量级设计,这有助于实际实施和部署.
- 提出的方法成功地平衡了高精度与降低计算需求.
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