基于多域信息融合的变速箱故障诊断方法
Fengyun Xie1,2,3, Gan Wang1, Jiandong Shang1
1School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究引入了一种使用多域信息融合的新变速箱故障诊断方法. 拟议的方法在识别变速箱故障方面实现了高精度,优于现有方法.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 传统的变速箱故障诊断依赖于人工经验,限制了准确性和效率.
- 开发自动化和准确的故障诊断方法对于工业机械至关重要.
- 现有的方法经常与复杂的振动模式和噪音作斗争.
研究的目的:
- 提出基于多域信息融合的变速箱故障诊断方法.
- 为了提高变速箱故障识别的准确性和可靠性.
- 克服传统基于经验的诊断方法的局限性.
主要方法:
- 建造了一个带有JZQ250固定轴变速箱和加速度传感器的实验平台.
- 预处理的振动信号使用单值分解 (SVD) 和短时间里叶变换 (STFT) 进行时间频率分析.
- 开发了一个多域信息融合卷积神经网络 (CNN) 模型,具有并行的1DCNN和2DCNN通道,融合特征和支持矢量机器 (SVM) 分类.
主要成果:
- 拟议的多域信息融合CNN模型实现了最高的故障识别准确率98.08%.
- 与FT-2DCNN,1DCNN-SVM和2DCNN-SVM方法相比,实验验证显示出更高的性能.
- t-SNE可视化证实了有效的特征提取和分类.
结论:
- 多域信息融合方法显著提高了变速箱故障诊断的准确性.
- 信号处理,CNN和SVM的综合方法为自动化变速箱健康监测提供了强大的解决方案.
- 这项研究为推进机械系统智能故障诊断提供了有希望的方向.
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