具有反色噪声的轻量级神经网络,用于使用深可分离卷积和转移学习进行轴承故障诊断
Jiping Li1, Nan Wang2, Dan Wang3
1School of Mechanical Engineering, Shenyang Urban Construction University, Shenyang, 110167, People's Republic of China. lijiping11@126.com.
Scientific reports
|December 29, 2025
概括
这项研究引入了轻量级的深度学习模型,用于轴承故障诊断,增强抗噪声和诊断速度. SqueezeNet-DLCNN在不到一分钟的时间内实现了97%的准确性,展示了卓越的性能.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 实际的轴承故障诊断面临着诸如噪音,性能下降和速度缓慢等挑战.
- 现有的方法可能与现实世界的条件和效率扎.
研究的目的:
- 开发高效,耐噪声的轻量级神经网络模型用于轴承故障诊断.
- 在不同的条件下评估这些模型的诊断和转移学习能力.
主要方法:
- 三个轻量级深卷积神经网络模型 (MobileNet-DLCNN,ShuffleNet-DLCNN,SqueezeNet-DLCNN) 使用深度可分离卷积来开发.
- 彩色噪声被添加到轴承故障数据集 (CWRU,MFPT) 中,用于抗噪训练.
- 比较实验评估了诊断准确性,速度和转移学习适应性.
主要成果:
- SqueezeNet-DLCNN显示出最好的诊断性能,在大约一分钟内达到97%的准确性.
- 拟议的轻量级模型表现出强烈的抗色噪声和适应不同数据集和工作条件的适应性.
- 与基线方法相比,这三种模型在抗噪声,效率和速度方面都表现出优势.
结论:
- 轻量级深卷积神经网络,特别是SqueezeNet-DLCNN,为高效和准确的轴承故障诊断提供了一个有希望的解决方案.
- 开发的模型具有抗噪声干扰的强度,并且可以适应各种操作场景.
- 这些发现强调了这些模型在实际工程应用中所具有的潜力,这些应用需要快速可靠的故障检测.
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