一个抗噪声卷积神经网络,用于基于多通道数据的轴承故障诊断
Wei-Tao Zhang1, Lu Liu1, Dan Cui1
1School of Electronic Engineering, Xidian University, Xi'an 710071, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种新型的多通道数据融合神经网络 (MCFNN),用于在杂的工业环境中增强轴承故障歧视. MCFNN显著提高了故障分类的准确性,即使有大量的背景噪声.
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:
- 工业轴承面临着变化的工作条件和不可避免的设备噪音.
- 现有的神经网络模型与噪音作斗争,影响轴承故障诊断的准确性.
研究的目的:
- 为了提高神经网络的抗噪声性能,用于轴承故障诊断.
- 开发一种可靠的方法,用于在现实世界中识别轴承故障,在杂的工业环境中.
主要方法:
- 使用信封时间频谱和3D过来提取故障特征的多通道样本表示.
- 一个多通道数据融合神经网络 (MCFNN) 结合了放弃培训.
- 使用不同旋转速度和负载在不同噪声条件下的数据集.
主要成果:
- 在无噪声环境中达到99.00%的故障分类准确度.
- 在噪音条件下表现出优异的性能,在0dB SNR下超过其他方法11.80%,在-4dB SNR下超过其他方法32.89%.
- 拟议的MCFNN有效地提取故障特征,并准确地分类轴承条件,尽管环境噪声.
结论:
- 拟议的MCFNN和多通道样本表示方法显著改善了在杂环境中进行轴承故障诊断.
- 该方法为需要可靠的轴承健康监测的现实世界工业应用提供了强大的解决方案.
- 这种方法提高了诊断系统的抗噪声能力,这对于工业机械维护至关重要.
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