基于Legendre多层层变换和卷积神经网络的Edge-IoT中的轮箱复合故障诊断
Xiaoyang Zheng1, Lei Chen1, Chengbo Yu2
1School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
一个新的轻量级神经网络LMWT-MCNN准确地诊断了边缘IoT环境中的变速箱复合故障. 这种方法即使在噪音条件下也能有效地识别故障,从而降低旋转机械诊断的成本.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 边缘计算和物联网 (edge-IoT) 正在迅速发展.
- 在边缘物联网系统中,轻量级网络开发对于变速箱复合故障诊断至关重要.
研究的目的:
- 设计一种新的,高精度的轻量级神经网络 (LMWT-MCNN),用于快速识别变速箱复合故障.
- 为了在边缘IoT环境中实现有效的故障诊断,降低旋转机械的劳动力成本.
主要方法:
- 在频率域中使用Legendre多波小变换 (LMWT) 来生成特征图像.
- 实现一个轻量级的多通道卷积神经网络 (MCNN),有三个卷积层和三个聚合层.
- 在完全连接层中的多标签分类用于特定故障类型的识别.
主要成果:
- LMWT-MCNN有效地提取有价值的故障特征,避免噪音干扰.
- 拟议的轻量级模型在识别复合故障类别方面实现了高精度.
- 对PHM 2009变速箱和帕德博恩大学轴承数据集的实验验证表明,在强噪声环境中具有稳定性.
结论:
- 在现实世界的边缘物联网应用中,LMWT-MCNN为旋转机械故障诊断提供了一个有前途的技术.
- 该方法提供了可靠和准确的复合故障识别,在噪音条件下优于现有的方法.
- 这种轻量级网络有助于降低劳动力成本,提高工业诊断的效率.
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