一个轻量级和高效的多模式功能融合网络用于工业应用中的轴承故障诊断
Chaoquan Mo1, Ke Huang1, Wenhan Li1
1College of Mechanical & Electrical Engineering, Wenzhou University, Wenzhou 325035, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
一个新的轻量级深度学习模型,轻量级和高效的多模式功能融合卷积神经网络 (LEMFN),增强了机械设备故障诊断. 它通过融合时间和频率域数据,在噪音条件下提高了准确性和稳定性.
科学领域:
- 机器学习 机器学习
- 机械工程 机械工程
- 信号处理 信号处理
背景情况:
- 机械设备的智能诊断模型面临着单输入功能,噪声灵敏度和大参数大小的挑战.
- 现有的方法往往在杂的环境中难以保持稳定性,也难以适应不同的操作条件.
研究的目的:
- 为改进机械设备故障诊断提出一个轻量级和高效的多模式功能融合卷积神经网络 (LEMFN).
- 提高特征学习能力,噪声强度和模型效率.
主要方法:
- 开发了一种轻量级和高效的多模式功能融合卷积神经网络 (LEMFN).
- 集成的时间域和频域信号用于多尺度特征提取.
- 整合了卷积块注意模块 (CBAM) 和随机重叠采样技术 (ROST).
- 采用特征融合策略,用于准确的故障诊断.
主要成果:
- 该LEMFN方法显示了高的诊断准确性和快速的趋同.
- 该模型在杂的环境中表现出强大的稳定性.
- 观察到提高了适应不同操作条件的适应性.
结论:
- 拟议的LEMFN方法有效地解决了现有的智能诊断模型的局限性.
- 多式联运数据和先进模块的集成提高了故障诊断性能.
- 开发了一个基于GUI的系统,促进了机械设备中智能故障诊断的实际应用.
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