通过Meta-BOHB优化CNN变压器模型和时间频域分析进行滚动轴承故障诊断
Yikang Wang1,2, He Jiang1,2, Baoqi Tong1,2
1School of Renewable Energy, Shenyang Institute of Engineering, Shenyang 110136, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究提出了使用变化模式分解 (VMD) 和深度学习的先进轴承故障诊断框架. 该方法实现了高精度和稳定性,提高了工业机械的可靠性.
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:
- 轴承故障在工业机械中至关重要,在准确和高效的诊断方面存在挑战.
- 现有的方法在准确性,模型复杂性和超参数调整方面扎.
研究的目的:
- 开发一个强大的,准确的诊断框架,用于轴承故障检测.
- 克服当前故障诊断技术的局限性.
主要方法:
- 利用变化模式分解 (VMD) 和快速里叶变换 (FFT) 来进行特征提取.
- 采用混合深度学习模型,将卷积神经网络 (CNN) 和变压器结合起来.
- 应用Meta-learning增强的贝叶斯优化和HyperBand (Meta-BOHB) 用于超参数优化.
主要成果:
- 在高稳定性 (±0.08%) 的CWRU数据集上实现了99.91%的平均分类准确性.
- 与现有方法相比,在精度,收率和损失指标方面表现出卓越的性能.
- 在高精度的CWRU,MFPT和PU数据集中验证了强大的泛化.
结论:
- 拟议的框架有效地解决了轴承故障检测方面的关键挑战.
- 该方法显示了提高工业可靠性和降低运营成本的巨大潜力.
- 先进的信号处理和优化的深度学习技术对于有效的故障诊断至关重要.
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