滚动轴承的故障诊断方法基于1D多通道改进的卷积神经网络在杂的环境中
Huijuan Guo1, Dongzhi Ping2, Lijun Wang2
1Department of Engineering, Huanghe Science and Technology University, Zhengzhou 450045, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
一个新的1D多通道改进卷积神经网络 (1DMCICNN) 提高了机械故障诊断的准确性. 这种方法有效地从噪音振动信号中提取特征,在降低计算负载的同时达到超过98%的准确性.
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:
- 机械设备的振动信号对于故障诊断至关重要.
- 环境噪音和干扰大大降低了诊断准确度.
- 现有的方法难以从噪音信号中提取特征.
研究的目的:
- 为在噪音环境中运行的机械设备提出一个强大的故障诊断方法.
- 通过深度学习提高故障诊断的准确性和效率.
- 为了应对从受污染的振动数据中提取有意义的特征的挑战.
主要方法:
- 一个1D多通道改进卷积神经网络 (1DMCICNN) 的开发.
- 整合了BiLSTM,注意力机制,以及具有局部稀疏结构的双通道CNN.
- 从噪音振动信号中提取多级特征信息.
主要成果:
- 在各种负载和速度条件下,1DMCICNN实现了高诊断精度 (98.67%至99.71%).
- 拟议的模型与未经优化的CNN相比,显示出更高的性能.
- 观察到训练参数显著减少了55.58%.
结论:
- 1DMCICNN是一种高效的方法,用于在噪音条件下准确的机械故障诊断.
- 整合BiLSTM和注意力机制改善了从振动信号中提取特征.
- 该模型为现实世界故障诊断应用提供了计算效率高和准确的解决方案.
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