轴承故障检测具有基于光滑扩展卷积的轻量级特征提取机制
1School of Electrical Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, 213000, China. pangyufeng121@163.com.
Scientific reports
|December 15, 2025
概括
一种新的轴承故障检测模型使用光滑扩展卷积和混合算法来减少参数和计算时间. 这种轻量级模型在资源有限的环境中实现了高精度的实时监控.
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:
- 传统的故障检测方法由于许多参数和复杂的计算,难以应对复杂的工作条件.
- 需要高效准确的轴承故障检测模型,特别是对于具有有限计算资源的实时应用.
研究的目的:
- 提出一种新的轴承故障检测模型,通过专注于模型轻量化和保持高检测精度来解决传统方法的局限性.
- 开发一种适用于在资源有限的情况下实时故障监测的模型.
主要方法:
- 拟议的模型整合了光滑扩展卷积,用于局部振动信号特征提取.
- 它使用组卷积,通道洗,网络修剪和知识蒸来减少计算复杂性和模型大小.
- 双向封闭的循环单元和生成对抗网络被结合起来,以捕捉数据中的长期依赖.
主要成果:
- 该模型显示,与现有方法相比,参数和推理时间显著减少,同时保持检测准确度.
- 在样本分类中,该模型实现了97.88%的准确性,274 fps的平均推理速度,1.66 FLOPs的计算成本和7.76M的参数.
- 对于轴承特征提取和故障检测,该模型达到96.13%的平均安装精度和99.62%的检测精度.
结论:
- 开发的模型有效地平衡了模型轻量化与强大的检测性能.
- 它非常适合实时轴承故障监控应用,特别是在计算能力和资源有限的环境中.
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